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Education andTraininginAutismandDevelopmentalDisabilities

Focusing on individuals withautism, intellectual disability and other developmental disabilities

Volume 53 Number 3DDDDAADD

September 2018

Education and Training in Autism and Developmental DisabilitiesThe Journal of the Division on Autism and Developmental Disabilities,The Council for Exceptional Children

Editor: Stanley H. ZuckerArizona State UniversityMary Lou Fulton Teachers College

Editorial Assistant: Stephanie McBride-SchreinerArizona State UniversityMary Lou Fulton Teachers College

Consulting EditorsMartin AgranKevin AyresDevender BandaJuliet Hart BarnettLaura BassetteKyle D. BennettEmily BouckAmanda BoutotMichael P. BradyStacy Carter

David CihakGinevra CourtadeSamuel A. DiGangiTeresa DoughtyKimberly W. FisherYoujia HuaBree JimenezRuss LangJustin LeafRose A. Mason

John McDonnellHedda Meadan-KaplanskyPam MimsWendy OakesJenny RootKarrie ShogrenTom E.C. SmithScott SparksFred SpoonerRobert Stodden

Keith StoreyWilliam TherrienMatt TincaniJason TraversToni Van LaarhovenElizabeth WestJohn WheelerMark WoleryLeah WoodDalun Zhang

Education and Training in Autism and Developmental Disabilities is sent to all members of the Division on Autism and DevelopmentalDisabilities of The Council for Exceptional Children. All Division members must first be members of The Council for Exceptional Children.Division membership dues are $30.00 for regular members and $15.00 for full time students. Membership is on a yearly basis. All inquiriesconcerning membership, subscription, advertising, etc. should be sent to the Division on Autism and Developmental Disabilities, 2900 CrystalDrive, Suite 100, Arlington, VA 22202-3556. Advertising rates are available upon request.

Manuscripts should be typed, double spaced, and sent (three copies) to the Editor: Stanley H. Zucker, Mary Lou Fulton Teachers College,Box 871811, Arizona State University, Tempe, AZ 85287-1811. Each manuscript should have a cover sheet that gives the names, affiliations, andcomplete addresses of all authors.

Editing policies are based on the Publication Manual, the American Psychological Association, 2009 revision. Additional information isprovided on the inside back cover. Any signed article is the personal expression of the author; likewise, any advertisement is the responsibilityof the advertiser. Neither necessarily carries Division endorsement unless specifically set forth by adopted resolution.

Education and Training in Autism and Developmental Disabilities is abstracted and indexed in Psychological Abstracts, PsycINFO, e-psyche,Abstracts for Social Workers, International Journal of Rehabilitation Research, Current Contents/Social and Behavioral Sciences, ExcerptaMedica, ISI Social Sciences Citation Index, Adolescent Mental Health Abstracts, Educational Administration Abstracts, Educational ResearchAbstracts, and Language and Language Behavior Abstracts. Additionally, it is annotated and indexed by the ERIC Clearinghouse onHandicapped and Gifted Children for publication in the monthly print index Current Index to Journals in Education and the quarterly index,Exceptional Child Education Resources. Access is also available in EBSCO, ProQuest, and JSTOR.

Education and Training in Autism and Developmental Disabilities Vol. 53, No. 3, September 2018, Copyright 2018 by the Division on Austimand Developmental Disabilities, The Council for Exceptional Children.

Division on Autism and Developmental DisabilitiesThe Council for Exceptional Children

Board of DirectorsOfficers

Past President Elizabeth WestPresident Jordan ShurrPresident-Elect Michael WehmeyerVice President Ginevra CourtadeSecretary Meaghan McCollowTreasurer Gardner Umbarger

Members

Autumn Eyre (Student Representative)Kimberly MaichNikki MurdickLeah Wood

Executive DirectorTeresa DoughtyPublications ChairMichael WehmeyerCommunications ChairEmily C. BouckConference CoordinatorCindy Perras

The purposes of this organization shall be to advance the education and welfare of persons with autism and developmental disabilities, researchin the education of persons with autism and developmental disabilities, competency of educators in this field, public understanding of autismand developmental disabilities, and legislation needed to help accomplish these goals. The Division shall encourage and promote professionalgrowth, research, and the dissemination and utilization of research findings.

EDUCATION AND TRAINING IN AUTISM AND DEVELOPMENTAL DISABILITIES (ISSN 2154-1647) (USPS 0016-8500) is pub-lished quarterly, by The Council for Exceptional Children, Division on Autism and Developmental Disabilities, 2900 Crystal Drive, Suite100, Arlington, Virginia 22202-3556. Members’ dues to The Council for Exceptional Children Division on Developmental Disabilitiesinclude $8.00 for subscription to EDUCATION AND TRAINING IN AUTISM AND DEVELOPMENTAL DISABILITIES. Subscriptionto EDUCATION AND TRAINING IN AUTISM AND DEVELOPMENTAL DISABILITIES is available without membership; Individual—U.S. $100.00 per year; Canada, PUAS, and all other countries $104.00; Institutions—U.S. $249.00 per year; Canada, PUAS, and all othercountries $254.00; single copy price is $40.00. U.S. Periodicals postage is paid at Arlington, Virginia 22204 and additional mailingoffices.

POSTMASTERS: Send address changes to EDUCATION AND TRAINING IN AUTISM AND DEVELOPMENTAL DISABILITIES,2900 Crystal Drive, Suite 100, Arlington, Virginia 22202-3556.

Preventing Bullying and Promoting Friendship for Students with ASD:Looking Back to Move Forward 243BROOKE N. WINCHELL, MELISSA A. SRECKOVIC, and TIA R. SCHULTZ

Exploring Post-School Outcomes across Time Out of School for Studentswith Autism Spectrum Disorder 253EMILY C. BOUCK and JIYOON PARK

Effects of Self-Mediated Video Modeling Compared to Video Self-Promptingfor Adolescents with Intellectual Disability 264SALLY B. SHEPLEY, KEVIN M. AYRES, RACHEL CAGLIANI, and ERINN WHITESIDE

High-Probability Request Sequence: An Evidence-Based Practice forIndividuals with Autism Spectrum Disorder 276CHELSI R. BROSH, LARRY B. FISHER, CHARLES L. WOOD, and DAVID W. TEST

Word Study Intervention for Students with ASD: A Multiple Baseline Study ofData-Based Individualization 287MICHAEL SOLIS, FARAH EL ZEIN, MARIE BLACK, ALEXANDRA MILLER,WILLIAM J. THERRIEN, and MARCIA INVERNIZZI

Comparing Different Delivery Modes for Literacy-Based BehavioralInterventions during Employment Training for College Students withDevelopmental Disabilities 299KALYNN HALL PISTORO, MICHAEL P. BRADY, KELLY KEARNEY, andANGELICA DOWNEY

Electronic Essay Writing with Postsecondary Students with Intellectual andDevelopmental Disabilities 311SUZANNE WOODS-GROVES, SAEED S. ALQAHTANI, KINGA BALINT-LANGEL,and AMANDA KERN

Teaching Students with Autism and Intellectual Disability to Solve AlgebraicWord Problems 325JENNY R. ROOT, BONNIE HENNING, and ERICA BOCCUMINI

Manuscripts Accepted for Future Publication in Education and Training inAutism and Developmental Disabilities 242

Education and Training in Autism and DevelopmentalDisabilities

VOLUME 53 NUMBER 3 SEPTEMBER 2018

The Division on Autism and Developmental Disabilities retains literary property rights on copyrighted articles. Upto 100 copies of the articles in this journal may be reproduced for nonprofit distribution without permission fromthe publisher. All other forms of reproduction require permission from the publisher.

Manuscripts Accepted for Future Publication in Educationand Training in Autism and Developmental Disabilities

December 2018

Effectiveness of video modeling presented via Smartboard for teaching social response behavior tochildren with autism. Esin Pektas� Karabekir and Nurgul Akmanoglu, Anadolu Universitesi, EngellileerArastirma Enstitusu, Yunus Emre Kampusu, Eskisehir, 26470 TURKEY.

Investigating components, benefits, and barriers of implementing community-based vocationalinstruction for students with intellectual disability in Saudi Arabia. Hussain A. Almalky, Depart-ment of Special Education, Prince Sattam bin Abdulaziz University, Al-Kharj, SAUDI ARABIA.

Effects of TEACCH structured teaching on independent work skills among individuals with severedisabilities. Inhwan Park and Yu-Ri Kim, Department of Education, Ewha Womans University, 52Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760 KOREA.

Improvements in proxy individualized education program meeting participation among Latinoparents. Meghan M. Burke, Kristina Rios, Brenda Lopez, Marlene Garcia and Sandra Magaña,University of Illinois at Urbana Champaign, 2288 Education Building, 1310 S. 6th Street, Cham-paign, IL 61820.

Participation in related services and behavioral, social, and adaptive symptom presentation in youngchildren with autism spectrum disorder. Stephanny F. N. Freeman, Tanya Paparella, Joanne J. Kim,Fiona Whelan, Gerhard Hellemann, and Steven Forness, UCLA, 78-243B Semel Institute for Neuro-science & Human Behavior, 760 Westwood Plaza, Los Angeles, CA 90024-1759.

Curricula to teach skills associated with self-determination: A review of existing research. Sheida K.Raley, Cristina Mumbardo-Adam, Karrie A. Shogren, David Simó-Pinatella and Climent Giné,Dept. of Special Education, Beach Center on Disability, 1052 Dole Human Developmental Center,1000 Sunnyside Ave., Lawrence, KS 66045.

Effects of a self-management intervention to improve behaviors of a child with fetal alcoholspectrum disorder. Megan M. Griffin and Susan R. Copeland, College of Education, University ofNew Mexico, 1 University of New Mexico, MSC05 3040, Albuquerque, NM 87131-0001.

Comparison of individualized and non-specific video-prompts to teach daily living skills to studentswith autism spectrum disorders. Sarah Domire Monaco and Pamela Wolfe, The College of NewJersey, Dept. of Special Education, Language, and Literacy, Education Building Room 312D,Ewing, NJ 08628.

Address is supplied for author in boldface type.

Preventing Bullying and Promoting Friendship for Studentswith ASD: Looking Back to Move Forward

Brooke N. WinchellUniversity of Wisconsin-Whitewater

Melissa A. SreckovicUniversity of Michigan – Flint

Tia R. SchultzUniversity of Wisconsin-Whitewater

Abstract: Now more than ever, students with autism spectrum disorder (ASD) are being taught in inclusivesettings. Despite inclusion initiatives, research suggests students with ASD have limited social interactions withneurotypical peers and have significantly fewer high quality friendships compared to neurotypical peers. Thepurpose of this paper is two-fold. First, we discuss how friendship development has been supported and targetedin school settings. Then, we discuss directions for future research and highlight the urgency of research in thisarea given the numerous negative outcomes associated with limited friendships and high rates of bullyingvictimization.

Since the initial establishment of the Educationfor All Handicapped Children Act of 1975, fed-eral legislation and school reforms have focusedon ways to provide equal educational opportu-nities for students with disabilities and provideaccess to the general education curriculum.Now more than ever, students with autism spec-trum disorder (ASD) are being taught in gen-eral education classrooms. In 2013, approxi-mately 60% of students with ASD were taughtfor 40% or more of their school day in generaleducation classrooms (NCES, 2016).

In inclusive environments more opportuni-ties occur for students with and without dis-abilities to interact (Mavropoulou & Sideridis,2014). However, despite increased access tomore inclusive environments and opportuni-ties to engage with peers, students with ASDoften experience difficulties accessing affordedrights within these inclusive environments. In-deed, two critical problems in the school con-text can severely impact the full participation ofstudents with ASD: limited, or lack of, friend-

ships with peers (Petrina, Carter, & Stephenson,2014) and frequent bullying victimization(Sreckovic, Brunsting, & Able, 2014).

Despite inclusion initiatives, research sug-gests students with ASD engage in limitedsocial interactions with peers and have sig-nificantly fewer reciprocal and high qualityfriendships compared to their typically devel-oping peers (Humphrey & Symes, 2011; Locke,Ishijima, Kasari, & London, 2010). When com-pared to their typically developing peers, stu-dents with ASD spend less time in cooperativeactivities and more time in solitary activities atschool (Bauminger, Shulman, & Agam, 2003;Humphrey & Symes, 2011; Wainscot, Naylor,Sutcliffe, Tantam, & Williams, 2008). Further,research consistently documents that youthand adolescents with ASD have poorer qualityfriendships, fewer reciprocal friendships, lesscontact with peers outside of school, and theduration of friendships are shorter comparedto their peers without ASD (see Petrina et al.,2014, for a review). Research suggests thatyouth with ASD educated in inclusive class-rooms are only involved in their peers’ socialrelationships approximately 50% of the time(Rotheram-Fuller, Kasari, Chamberlain, &Locke, 2010). As students enter middle andhigh school they become even less connected.In a nationally representative longitudinal

Correspondence concerning this article shouldbe addressed to Brooke N. Winchell, Department ofSpecial Education, University of Wisconsin-White-water, 800 W. Main Street, Whitewater, WI 53190.E-mail: [email protected]. Telephone: 262–472-5809.

Education and Training in Autism and Developmental Disabilities, 2018, 53(3), 243–252© Division on Autism and Developmental Disabilities

Promoting Friendship for Students with ASD / 243

mailto:[email protected]

study of the involvement of adolescent stu-dents with disabilities, students with ASD werereported as the least likely to frequently seefriends outside of school, receive telephonecalls from friends, and get invited to anotherstudent’s social event (Wagner, Cadwallader,Garza, & Cameto, 2004). Both educators andfamilies have expressed concern about thelack of opportunities school-age children withASD have for building friendships with theirpeers in schools (Bauminger et al., 2003; Boyd& Shaw, 2010; Kasari, Locke, Gulsrud, & Ro-theram-Fuller, 2011). And individuals withASD have indicated they want to develop so-cial relationships (Daniel & Billingsley, 2010;Muller, Schuler, & Yates, 2008).

It may not be surprising youth with ASDstruggle to develop relationships with peersbecause by definition individuals with ASDhave challenges in social communication andinteraction (APA, 2013). Students with ASDoften have difficulty initiating and respondingto peers. For example, students with ASD mayhave difficulty introducing topics of interestand relevance, using age appropriate greet-ings, and understanding facial expressions,humor, and other people’s feelings (Paul, Or-lovski, Marcinko, & Volkmar, 2009; Weiss &Harris, 2001). Some students with ASD whohave narrow interests may monopolize a con-versation and not allow others to contributetheir thoughts and/or interests resulting in anunbalanced conversation. Researchers havealso noted an initial increase in aggression,self-injurious behavior, anxiety, and depres-sion during adolescence (Hammond & Hoff-man, 2014; Schall & McDonough, 2010),which may further make engaging in positivesocial interactions difficult. These deficits insocial interaction and communication can di-rectly impact friendship development and bul-lying victimization. In fact, research suggestsfriendships can serve as a protective factoragainst bullying victimization (Boulton, True-man, Chau, Whitehand, & Amatya, 1999;Hodges, Boivin, Vitaro, & Bukowski, 1999),therefore social interaction and communica-tion deficits coupled with few friendships in-creases the risk students with ASD are bulliedby peers (Sreckovic et al., 2014).

In fact, students with ASD have been re-ported to experience bullying victimizationmore than the general population (e.g., Little,

2002) and more than their peers with otherdisabilities (e.g., Humphrey & Symes, 2010;Rowley et al. 2012; Twyman et al., 2010).While prevalence of bullying victimizationrates among youth with ASD range acrossstudies, a review indicated prevalence esti-mates reported within the past year to be be-tween 46% and 94% across studies (Sreckovicet al., 2014). In contrast, bullying victimizationrates among the general adolescent popula-tion in the United States in 2011 were esti-mated to be 28% (Robers, Kemp, & Truman2013). Research suggests students with ASDare more commonly victims than bullies orbully-victims (Zablotsky, Bradshaw, Anderson,& Law, 2013). This may not be surprisinggiven that the profiles of students with ASDare often very similar to the profiles of bothprovocative and passive victims (e.g., have lim-ited friendships, show signs of low self-esteem;Olweus, 1993; Orpinas & Horne, 2006; So-fronoff, Dark, & Stone, 2011). Many of thecommon characteristics associated with vic-tims of bullying are also characteristics com-monly used to describe students with ASD(Sreckovic et al., 2014). The greatest risk fac-tors for bullying include young age, commu-nication difficulty, internalizing mental healthconcerns, having parents with mental healthconcerns, and having few friends (Cappado-cia, Weiss, & Pepler, 2012).

The limited positive social interactions andfriendships students with ASD have with theirpeers are concerning considering interactionsand relationships with peers can make impor-tant contributions not only to their overallquality of life, but to their success in school,as well (Rubin, Bukowski, & Laursen, 2009).During school, rejected students are less likelyto participate in classroom activities and morelikely to perform poorly on achievement as-sessments (Buhs & Ladd, 2001). Peer rejec-tion can also lead to an array of psychologicalproblems including loneliness (Parker &Asher, 1993) and internalizing and external-izing behavioral problems (Ladd, 2006). Fur-ther, adolescents with poor social adjustmentare at risk for school drop-out and delin-quency (Parker & Asher, 1987). For individu-als with ASD, social-related problems canpersist into adulthood and make navigatingwork and community relationships challenging(Sperry & Mesibov, 2005). On the contrary,

244 / Education and Training in Autism and Developmental Disabilities-September 2018

research suggests adults with ASD who havegreater quantity and quality of friendshipsreport lower levels of loneliness (Mazurek,2014).

When these individuals are subjected to bul-lying victimization, the negative consequencesbecome even greater. Bullying victimizationcan lead to numerous negative consequences,including but not limited to, developing inter-nalizing and externalizing behavior problems,dropping out of school, and suicide (Olweus,1993; Parker & Asher, 1987). Victimizationhas been linked to an array of internalizingbehavioral problems, including loneliness,anxiety, depression, negative self-concept,and low self-esteem (Hawker & Boulton,2000). Students with ASD who have experi-enced victimization have been reported toexperience physical injuries and emotionaltrauma and feeling scared for their safety(Zablotsky et al., 2013). The National AutisticSociety reported students with ASD who werevictimized suffered from damaged self-esteem,poorer school work quality, and negative im-pacts on mental health, social skills, and rela-tionships (Reid & Batten, 2006). More than30% of students with ASD missed school andalmost 20% changed schools due to bullying(Reid & Batten, 2006). Moreover, peer victim-ization has been identified as a risk factor ofsuicidality in individuals with ASD (Segers &Rawana, 2014).

Intervening to protect students with ASDfrom experiencing these negative conse-quences is imperative. The purpose of thispaper is two-fold. First, we discuss how friend-ship development has been supported andtargeted in school settings. Then, we discussdirections for future research and highlightthe urgency of research in this area given thenumerous negative outcomes associated withfew friendships and high rates of bullying vic-timization.

Supporting Friendship Development

Authentic friendships play a crucial role forchildren with disabilities to achieve success-ful outcomes across their lifespan (Schuh,Sundar, & Hagner, 2015). With a higher prev-alence of students with ASD in the classroom,concerns for opportunities to create socialnetworks, specifically individual friendships is

increasing as well (Rotheram-Fuller et al.,2010). Statistically, this is reported as equiva-lent among neurotypical peers, as only halfreport having friends with disabilities (Dyson,2005). This could be due in part to the factthat both students with and without ASD needhelp to develop social communication skillsthat are needed for friendships (Diamond &Tu, 2009; Jones & Howley, 2010).

Many traditional definitions of “friendship”include the construct of reciprocity. Whilethese relationships should be meaningful toboth parties, friendships may have a differentmeaning for children with ASD. There is evi-dence that children with ASD may have diffi-culty distinguishing the role of friendship in away similar to the manner in which a neuro-typical child would identify friendship (Baum-inger & Kasari, 2000). Specifically, childrenwith ASD are less able to identify qualities offriendships and struggle to understand thesocial-emotional components of loneliness(Bauminger & Kasari, 2000). Moreover, in-dividuals with ASD understand loneliness dif-ferently and experience loneliness more in-tensely and more frequently than neurotypicalchildren. Additionally, it has been discoveredthat even when friendships are present, thequality of friendships are suffering (Bollmer,Milich, Harris, & Maras, 2005; Locke et al.,2010). Specifically, the level of companionshipand helpfulness is of less benefit, and the friend-ships are shorter and less stable than friendshipbetween neurotypical peers (Bauminger et al.,2003; Rowley et al., 2012). Also, contributing tolower quality of friendships is the short length offriendship.

Inclusion

A review of 20 studies in seven countries ex-amining students’ attitudes towards peers withdisabilities found that students generally holdneutral attitudes toward peers with disabilities(de Boer, Pijl, & Minnaert, 2012). Youngerchildren often have positive attitudes towardpersons with disabilities (Hong, Kwon, & Jeon,2014). The study investigated children’s will-ingness to include peers with disabilities insocial settings. It was concluded that kinder-garteners’ understanding of disabilities andexperiences interacting with individuals withexceptionalities had an impact on their will-


ingness to include a peer in specific socialsituations (Hong et al., 2014). Connectionsbetween attitudes and social interactionswhich may promote friendships has beenexamined for some time (e.g., Diamond &Hestenes, 1994). Researchers analyzed severalstudies examining young children’s attitudesin relation to intended behaviors (Yu, Os-trosky, & Fowler, 2012) which suggests obser-vations and interactions with the peers in aninclusive setting can impact the ability to rec-ognize and understand their peers with dis-abilities better.

Even if neurotypical peers possess positiveviews of peers with disability, it does not alwaystranslate into developing authentic friend-ships (Dyson, 2005). Characteristics of chil-dren with disabilities can contribute to neuro-typical peers’ willingness to include classmatesin social contexts. Children with disabilitiesare alienated from their social peer groupwhen aggressive behavior, limited social com-munication and motor skills are prevalent(Odom, 2005). Although, inclusive classroomshave been found to be the place of origin forfriendships between children with and with-out disabilities (Bauminger et al., 2003), prox-imity itself is not an effective catalyst for au-thentic connection. In inclusive classrooms,children with ASD are only involved in peers’social relationships about half of the time andappear to interact even less as the grade levelincreases (Rotheram-Fuller et al., 2010).Therefore, the mere presence of childrenwith disabilities in the same learning envi-ronments as neurotypical peers is unlikely toorganically create connections between stu-dents of diverse abilities (e.g., Diamond &Tu, 2009).

Friendship Interventions

Most interventions aiming to promote friend-ship, teach specific social skills to the individ-ual with ASD. Examples of outcome measuresinclude social interactions, conversation skills,and perspective taking (Reichow & Volkmar,2009). Typically, interventions focus on improv-ing social and emotional skills only for thestudents with ASD (Bellini, Peters, Benner, &Hopf, 2007), and fall into one of two generalcategories: Social Skills Training (SST) or Peer-Mediated Instruction/Intervention (PMII).

Social Skills Training. Social skills training(SST) can include group or individual teach-ing of specific social skills and is considered anevidence based practice for individuals withASD (Wong et al., 2014). Individual interven-tions are carried out with a professional andan individual with ASD addressing the individ-ual’s specific skill needs (Griffin, Sam, &AFFIRM Team, 2016). Each program is indi-vidualized and typically evaluated with single-case design methods. While this type of inter-vention can yield improvements in socialskills, it can be resource intensive and difficultto generalize skills to social settings.

Social skill group interventions can addressthe need for using resources more efficientlythan individual interventions. Further, groupinterventions allow for opportunities for prac-ticing social skills. In their meta-analysis ofsocial skill group interventions, Gates, Kang,& Lerner (2017) reported a significant vari-ability in the characteristics of social skillgroups. Participants can range in age, thoughmost groups focus on a small range within agroup. Group interventions vary in how theyare structured; some focus on social skills in-struction, while others provide a context forsocial interactions to take place. Groups alsovary in their length (a few weeks to 2 years)and intensity (1 hour sessions to 6 hour ses-sions; Gates et al., 2017). Group social skillinterventions are associated with moderateimprovements in social competence. How-ever, most programs do not specifically mea-sure friendship as an outcome.

Peer-mediated Instruction/Interventions (PMII).Peer-mediated instruction/interventions (PMII)are a group of interventions in which peersare the intervention agents engaging studentswith disabilities to help them learn new skills(Chan et al., 2009; Sperry et al., 2010; Strain &Odom, 1986). PMII is rooted in both behav-iorism and social learning theory (Sperry etal., 2010) and is considered an evidence-basedpractice, according the National ProfessionalDevelopment Center on Autism (Wong et al.,2014). It has been shown to be effective inpromoting social and academic skills for stu-dents with a variety of disabilities (Bass & Mu-lick, 2007; Carter, Sisco, Chung, & Stanton-Chapman, 2010) and has been especiallyeffective for students with ASD (Zhang &Wheeler, 2011). In a systematic review of


PMII, Chan and colleagues (2009) found that91% of the studies they evaluated yielded pos-itive outcomes for students with ASD. Further,while friendship development has not been acommon dependent variable in PMII studies,some studies have explored the impact ofPMII on friendship and found friendships be-tween students with ASD and their peers haveformed (e.g., Gardner et al., 2014; Sreckovic,Hume, & Able, 2017).

Anti-bullying Interventions

Anti-bullying interventions vary in the types ofstrategies and supports used to reduce bully-ing (Merrell, Gueldner, Ross, & Isava, 2008).Programs may include staff training, studenttraining, environmental changes, and/or pol-icy changes. In their meta-analysis of anti-bul-lying programs, Merrell and colleagues (2008)concluded that most anti-bully programs wereassociated with some positive outcomes, butthe effect sizes were weak. Additionally, theyfound that a small number of anti-bullyingprograms had negative effects, meaning thatbullying increased after the intervention. Mostof the intervention studies have not been rep-licated to determine the efficacy of specificprograms. Further, most programs rely onstudent and teacher report of anti-bullyingknowledge as opposed to observation of bul-lying behavior (Merrell et al., 2008). In an-other review, Ttofi and Farrington (2011)found that the most effective programs wereintensive, long in duration, included parentmeetings, included high levels of supervisionduring recess, and upheld firm disciplinaryconsequences for bullying. Both articles con-cluded that more research is needed to iden-tify evidence-based anti-bully programs. Ofthe reviewed studies only one program mea-sured positive peer interactions and one mea-sured peer acceptance (Merrell et al., 2008).

Within the area of anti-bullying programs,most are not specific to supporting studentswith disabilities who are victims of bullying. Ina recent systematic review, only six studieswere identified that focused on bullying andstudents with disabilities (Houchins, Oakes, &Johnson, 2016). Of these six studies only onemet all eight Council for Exceptional Chil-dren (2014) quality indicators and two studiesmet seven of the eight quality indicators.

None of these interventions were specific tostudents with ASD nor did they measurefriendship development.

Future Directions for Research and Practice

Most research and interventions aimed at im-proving social situations for individuals withASD focus on improving social skills of stu-dents with ASD or on preventing bullying.The most recent efforts to combat bullyingfocus on training educators on defining, rec-ognizing, preventing the occurrence of bully-ing, while social skills interventions focus pri-marily on skill development of students withASD (Bellini et al., 2007; Schneider, Gold-stein, & Parker, 2008). While both of thesetypes of interventions address important ar-eas, they do not specifically promote friend-ship development. Given the numerous nega-tive consequences associated with limited, orlack of, friendships and bullying victimization,it is imperative that research expands in thesecritical domains specifically for students withASD. While many school districts are trying toput practices in place, particularly to reducebullying, they often address the general pop-ulation and do not meet the multifacetedneeds of students with ASD. School adminis-trators need guidance from research on effi-cacious practices to help individuals with ASDfoster and maintain friendships and reducerates of bullying. In the following section wehighlight several important areas of future re-search. Specifically, we emphasize the needfor urgency in reducing victimization andhighlight the gap between the promoting andmaintaining authentic friendships within ourcurrent evidence-based social interventions,and the inherent need to gather the voicesand perspectives of student with ASD relatingto developing meaningful friendships.

Context and Role of Schools in SupportingStudents

Future research needs to evaluate the directimpact of anti-bully programs on students withASD. Current anti-bully programs are evalu-ated on a variety of variables, but typically donot specifically assess outcomes for studentswith disabilities. Future research should mea-sure if and how well anti-bully programs re-


duce bullying for students with ASD. Becauseresearch indicates that most anti-bully pro-grams are only moderately successful (Merrellet al., 2008), future interventions could addelements specific to reducing bullying for stu-dents with ASD. For example, one elementthat may possibly improve anti-bullying pro-grams could be to use befriending interven-tions. As discussed earlier in this paper, stu-dents with ASD often struggle with social skills(APA, 2013) and could benefit from skills spe-cifically targeting friendship development.Anti-bully programs should be coupled withpeer sensitivity training or other programs thatpromote friendship.

Knowledge children have about ASD andattitudes they hold towards persons with ASDimpact their willingness to interact with peerswith ASD (Bagwell & Schmidt, 2011; de Boeret al., 2014). For friendships to flourish, thefocus of trainings should not only be on stu-dents with ASD. Neurotypical peers need tobetter understand and accept their peers withASD (Meyer & Ostrosky, 2014) and moretrainings targeted for neurotypical childrenare needed. Because of the limited literatureabout school-based peer sensitivity trainings, itis important to examine how students canbenefit from ASD awareness training and un-derstand how the training students receivechange their attitudes and beliefs about theirpeers with ASD (Rossetti, 2011). There is evi-dence demonstrating that when studentsreceive information through multi-modal pre-sentations (e.g. workshops, videos) their atti-tudes towards peers with ASD improve (Ros-setti, 2011). In particular, when studentsreceive training about characteristics of ASDand learn about strategies for interactingwith peers with ASD while pointing out theirstrengths, students will develop better knowl-edge about children with ASD and increasetheir intentions for interacting with theirpeers (Silton, 2009). Kasari and colleagues(2011) found that six weeks of peer trainingshowed significant improvements in the num-ber of peers with ASD students nominated,the social skills classroom teachers reported instudents, and a decline of incidents of isolatedplay on the playground for children with ASD.

A critical first step is removing barriers tounderstanding that similarities should be pri-oritized over differences, as well as minimizing

perceptions of lower status or ability level, isessential to creating authentic connectionsleading to friendship (Finke, 2016). The im-pact of anti-bullying programs and peer sensi-tivity training together need to be evaluated toassess the impact on friendship.

Further, once effective interventions are im-plemented and friendships are formed be-tween students with ASD and their peers it isimperative to examine the longevity of thosefriendships. In other words, does the friend-ship continue after the intervention is imple-mented and what does the friendship looklike? Research is also needed to examine thedirect effects of the friendships on the psycho-logical well-being of individuals with ASD, asresearch has identified the negative conse-quences of not having positive peer relation-ships, including loneliness (Parker & Asher,1993) and internalizing and externalizing be-havior problems (Ladd, 2006). As stated abovethe friendship may look different than a “typ-ical” friendship, but the student with ASD maystill benefit from the positive consequences ofhaving a friend regardless if the friendship fitsthe “typical model” of a friendship.

Perspectives of Students with ASD

Future inquiry is also needed to better under-stand the effects of diagnosis disclosure on peerrelationships, acceptance, and bullying. TheReciprocal Effects Peer Interaction Model de-veloped by Humphrey and Symes (2011) as-serts that lack of awareness and understandingof ASD can result in limited peer friendshipsand social networks and increased bullyingvictimization. Yet, few studies have examinedthe impact of increased awareness and under-standing of ASD in the form of diagnosis dis-closure. Campbell (2007) investigated the ef-fects of educational messages delivered tomiddle school students regarding a video ofan unfamiliar student displaying ASD-like be-haviors. Results indicated students who wereunfamiliar with ASD reported more favorableattitudes when they were provided with bothexplanatory and descriptive information.Students with prior knowledge of ASD re-ported more positive attitudes regardless ofthe message. Research also suggests the per-son delivering information about ASD (e.g.,doctor, mother) may impact student atti-


tudes (Morton & Campbell, 2008). This topicmay be difficult to investigate because empir-ical information on diagnosis disclosure issparse. Therefore, we encourage researchteams to interview individuals with ASD whohave already disclosed their diagnosis to theirpeer group, as well as interview their familiesand teachers, to gain a better understandingof their experience, including how they dis-closed their diagnosis and what happened im-mediately after and over time.

Individuals with ASD have been found tocomprehend some aspects of friendship, butare less equipped than neurotypically develop-ing peers (Bauminger & Kasari, 2000) to ad-vocate for how friendship may look like in real-life situations (Finke, 2016). This may be duein part to the lack of opportunity to interactand build ongoing relationships with peers(Daniel & Billingsley, 2010; Finke 2016). Thelack of experience limits the ability to trulyunderstand how connections are initiatedand the roles individuals play in creating andmaintaining friendships.

An important point to make is that tradi-tional definitions of friendship include theterms “reciprocity” and “mutual” (Bagwell &Schmidt, 2011; Bauminger-Zviely, 2013). Forindividuals with ASD, the focus should be lesson these terms and more on “transactional”.Friendship can look or play out in many ways,as longs as both parties find benefit from theinteraction then it is worthy to be viewed as anauthentic connection. The characteristics and‘deliverables’ do not need to be of equal iden-tity or value. As a field, we need to gatherinformation from students with ASD aboutwhat is most meaningful in a friendship. Whatdoes a high-quality friendship mean? Whichactivities do they wish to engage in? What willpromote an interaction? Sustain an interac-tion? Once we know the motivation for indi-viduals to engage in friendship-building activ-ities we can create a positive environment inwhich to elicit such interactions among stu-dents with diverse needs.

Conclusion

Individuals with ASD often experience bully-ing victimization and have few, if any, friend-ships. These two critical problems can resultin a multitude of internalizing and externaliz-

ing behavioral problems. It is imperative thatresearch expand on efforts to help individualswith ASD foster and maintain friendships andreduce rates of bullying victimization. All chil-dren deserve to benefit from a high qualityeducation, including positive academic, so-cial, and behavioral gains, and students withASD are no exception.

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Received: 21 June 2017Initial Acceptance: 16 August 2017Final Acceptance: 11 October 2017


Exploring Post-School Outcomes across Time Out of Schoolfor Students with Autism Spectrum Disorder

Emily C. Bouck and Jiyoon ParkMichigan State University

Abstract: While one generally assumes the longer one is out of school the more positive his or her outcomes thismay be, this may not be accurate. Little research systematically explores the relationship between time after exitinghigh school and post-school outcomes, especially for students with disabilities. This study represented a secondaryanalysis of the NLTS2 aimed at understanding the immediate and long-term post-school outcomes of studentswith autism spectrum disorder (ASD) and the relationship among those outcomes. The main findings suggestpositive results for some post-school outcomes examined when considering improved success the longer one wasout of school (e.g., attending and graduating from post-secondary education and employment). However,inconsistent, at best, and negative, at worst, results were found for working full time and wage earning thelonger one with ASD is out of high school.

Exiting high school is an exciting – and criti-cal – time for secondary students, often asso-ciated with increased freedom, responsibili-ties, and opportunities (Heckhausen, Chang,Greenberger, & Chen, 2013). In theory, stu-dents engage in choices regarding their fu-ture, including attending post-secondary edu-cation or obtaining employment; studentsmay also select their living arrangements, suchas living independently from their parents(Guo, Marsh, Morin, Parker, & Kaur, 2015).Data from the National Center for EducationStatistics and the Institute of Education Sci-ences (IES) suggest 25.2 million students areenrolled as undergraduates in the UnitedStates, with 42.2% of those attending publictwo-year institutions and 31.4% attendingpublic four-year institutions (Ginder & Kelly-Reid, 2013). Typically, if one does not attenda post-secondary institution, one enters thejob market. Recent data from the UnitedStates Department of Labor indicated 38.8%of 18–19 year olds and 62.9% of 20–24 yearolds were employed in labor market (BureauLabor Statistics, 2014).

Beyond the post-school experiences for stu-

dents in general, particular attention is paid tothe post-school outcomes of students with dis-abilities. Historically, students with disabilitiesstruggle during their years after high school(i.e., adult years), including experiencinglower rates of attendance at post-secondaryinstitutions, lower rates of employment, andlower rates of independent living (Blackorby& Wagner, 1996). More recent data suggestattention is still needed to the post-school expe-riences of students with disabilities. Newman,Wagner, Cameto, & Knokey (2009) found thatonly 45% of students with disabilities attendedany type of postsecondary education within fouryears of leaving school. They also reported thatalthough 73% of students with disabilities re-ported being employed sometime after exitingschool, only 58% were doing so full time. Fi-nally, Newman et al. found only 25% of studentswith disabilities lived independently within fouryears after high school.

Given the heterogeneous nature of studentswith disabilities, often post-school outcomesare disaggregated by disability category (e.g.,students with learning disabilities or autismspectrum disorder) to understand the differ-ent experiences. Previous research on out-comes for students with autism spectrum dis-order (ASD), specifically, suggests less positivepost-school experiences than students withdisabilities in general. Shattuck, Narendorf,

Correspondence concerning this article shouldbe addressed to Emily C. Bouck, 620 Farm Lane,Michigan State University, East Lansing, MI 48824.E-mail: [email protected]


ASD Outcomes / 253


Cooper, Sterzing, Wagner, & Taylor (2012)found that within six years of exiting highschool, only 55.1% of students with ASD wereever employed for pay and 34.7% had everattended a two- or-four year college. In con-trast, approximately one-third of students withASD have been neither employed nor attend-ing postsecondary education within six yearsof leaving high school. Students with ASD alsoreport low rates of independent living; New-man et al. (2011) found students with ASDexperienced the lowest rates of independentliving (i.e., not living with parents, guardiansor another family member, aside from aspouse). Anderson, Shattuck, Cooper, Roux,and Wagner (2014) reported that less thanone-fifth of students with ASD had ever livedindependently after high school. These out-comes are disconcerting; additional researchis needed and warranted on the post-schooloutcomes of individuals with ASD (Roux et al.,2013).

Post-School Outcomes across Time

Typically, one assumes the longer s/he is outof high school, the better his/her outcomes.In other words, it is logical to assume that onewould make more money the longer one isout of high school – given additional time toobtain full-time employment and/or graduatefrom post-secondary education – as well as bemore likely to live on one’s own (Carnevale,2013). These are not unique expectations, butlikely universal expectations for students withand without disabilities, although patterns areshaped by societal context (e.g., recessions)and generational factors (e.g., millennials vs.Generation X; The Council of Economic Ad-visers, 2014). However, one presumes mostindividuals expect their lives will improve thelonger one is out of high school.

Previous researchers suggested a larger per-centage of students with disabilities report ex-periencing adult life outcomes the longer theyare out of school. For example, 24.7% of stu-dents with disabilities report living indepen-dently within four years of exiting high school,as compared to 35.7% of students with disabil-ities within six years and 44.7% within eightyears. Likewise, the percentage of studentswith disabilities who indicate ever attendingpostsecondary education increased the longer

the students were out of school, going from44.7% (within four years) to 54.9% (within sixyears) and 60.1% (within eight years) (New-man et al., 2009; Newman et al., 2011; Sanfordet al., 2011). However, the pattern was incon-sistent for being currently employed, increas-ing from with four years of high school towithin six years of high school (56.8% and71.1%, respectively) and dropping for withineight years of high school (60.2%). For specif-ically students with mild intellectual disability,researchers also found inconsistent patternsfor improvement of post-school outcomes thelonger students were out of high school (Bouck,2014). Yet, little-to-no research exists which ex-plores the post-school outcome data pattern forindividuals with ASD.

This particular research project – represent-ing a secondary analysis of the National Lon-gitudinal Transition Study-2 (NLTS2) data –sought to explore the relationship betweenlength of time from exiting school and post-school outcomes for students with ASD. Giventhe unique characteristics of students withASD relative to employment, postsecondaryeducation, and independent living, the re-searchers felt it was important to isolate andexplore the longitudinal nature of adult lifeoutcomes for individuals with ASD. The spe-cific research questions included (a) what arethe immediate post-school outcomes of stu-dents with ASD; (b) what are the longer-termpost-school outcomes of students with ASD;and (c) how do longer-term outcomes of stu-dents with ASD compare to the more imme-diate outcomes?

Method

In compliance with the regulations from theUnited States Department of Education Insti-tute of Education Sciences (IES), no data forwhich the raw, unweighted sample size is lessthan three are reported. No raw data are re-ported in this article; data are reported asweighted using the mechanism provided forthe NTLS2 by the original data collectors –SRI International (see Javitz & Wagner, 2003;Wagner, Newman, Cameto, Garza, & Levine,2005 for a more in-depth discussion of weight-ing the data). Each survey has weights for eachcase within the database; the weighting ac-counts for the sample in the data within the


larger population from participating schoolsand districts (IES, n.d.). Weighting the data al-lows for a discussion of the population ratherthan the sample; the weighted data were ana-lyzed using the Complex Samples packagewithin SPSS.

Participants

A total of 4,665 secondary students with ASDare represented within these data. The major-ity of those individuals were male (93.8%, SE1.9). The majority were also Caucasian (58.7%,SE 8.8), followed by African-American(36.7%, SE 8.5), Asian (2.2%, SE 1.7) andHispanic (2.1%, SE 1.3). Given the focus onpost-school outcomes, the majority of studentswere 17–18 years of age when in school (56%,SE 8.9), followed by 14 (19.1%, SE 3.7), 16(16%, SE 4.1) and then 15 (8.8, SE 2.4). Al-though the majority of students identified asnative English speakers (68%, SE 8.1), 1.3%(SE 0.3) indicated they were English languagelearners or bilingual and 30.8% (SE 8.0) didnot produce verbal speech, as reported byparents or students themselves. The majorityof the students were from families with annualincome between $25,000 and $50,000 (54.8%,SE 10.3), followed by greater than $50,000(28.3%, SE 6.7) and then less than $25,000(16.8%, SE 7.9). Over half of the studentslived in suburban communities (60.4%, SE8.7), followed by urban (36.5%, SE 8.1) andthen rural (3.1%, SE 1.3).

Data Collection

Data from this secondary analysis of theNLTS2 came from waves 1–4 of original datacollection. Each wave represented a two-yearperiod of data collection; the original NLTS2data collection occurred across 10 years andfive waves. Wave 1 data were collected in 2001and 2002; wave 4 data collected in 2006 and2007. Data from wave 1 and wave 2 representin-school experiences and data from waves 2,3, and 4 represent post-school experiences.Hence, the post-school experiences are dis-cussed as within two years (waves 2 and 3),within four years (waves 3 and 4), and withinsix years of exiting high school (wave 4).

The data for the secondary analysis camefrom two of the six types of data collection: the

parent/youth survey and the school programsurvey. The parent/youth survey was typicallya 60-minute phone interview, completed byparents in wave 1 but, generally, by students insubsequent waves. However, if a student wasunable to respond to the questions, a parentcompleted the interview in waves 2–4. Also, ifparticipation in a phone interview was notpossible, parents and/or youth were mailed asurvey. The parent/youth survey provided de-mographic variables (e.g., gender, ethnicity)as well as variables representing the post-school experiences (e.g., employment, post-secondary education). The school programsurvey was a mail survey completed by theteacher most familiar with a student’s pro-gram. The school program survey solicitedinformation relative to a student’s school ex-periences, such as curriculum and transitionplanning as well as demographics.

Procedure

To complete the secondary analysis, the re-searchers determined the variables needed toanswer the research questions from the twosurvey instruments: the parent/youth survey(i.e., demographics and post-school experi-ences) and the school program survey (i.e.,demographics). In terms of post-school expe-riences, the authors sought to represent inde-pendent living (i.e., living independently ordependently), employment (i.e., employed –defined as having a paid job, full time/parttime employment, wage), and post-secondaryeducation (i.e., attending and earning adiploma from four-year, community college,and/or vocational/technical school). Next,the researchers created in-school and post-school databases from the respective surveys;variables not used in the secondary analysiswere deleted. The authors created in-schooldatabases for waves 1 and 2 by first creatingseparate in-school databases for wave 1 forboth parent/youth and school program sur-veys and then for both in wave 2. The two waveone in-school databases were merged togetheras were the two in-school databases from wave2, resulting in a complete wave 1 in-schooldatabase and a complete wave 2 in-school da-tabase. The authors then created separate out-of-school parent/youth databases for waves 2,3, and 4. Next, the authors merged databases

ASD Outcomes / 255

to represent in school in wave 1 and out inwaves 2 (within two years), 3 (within fouryears) and 4 (within six years of high school);the same was done for the in school in wave 2and out in waves 3 (within two years) and 4(within four years of high school). The au-thors used Complex Samples with SPSS 22 tomerge via cases. To create one database, theauthors then renamed all the variables withinthe two in and out databases to be identicaland merged the two databases with ComplexSamples via variables.

Data Analysis

To answer the research questions, the authorsused frequency distributions and a test equiv-alent to a F-test. All analyses were conductedwith SPSS 22 using Complex Samples (SPSS22) and Excel (the equivalency to a F-test).Specifically, to answer research questions 1and 2 – the immediate (i.e., within two years)and longer-term post-school outcomes (i.e.,within four and six years) of students withASD – the authors conducted frequency dis-tributions on the variables of interest: attend-ing and earning a diploma from (a) a commu-nity college, (b) vocational/technical school,and (c) a four-year institution; ever having apaid job within the period between surveys(i.e., generally two years), currently having apaid job; living independently; hourly wage;working full or part time; liking work; whofound individual his/her job; and how onegets oneself to work (i.e., drives self, rides withfamily, uses public transportation, or walks/rides the bus). Please note, that due to studentor parent preference, inherent skip logicwithin the survey, and attrition, not everyquestion was asked and/or answered. Hence,the frequencies as reported are based out ofthe weighted number of individuals who re-sponded to that question.

To answer the third research question –how the longer-term outcomes of studentswith ASD compare to the more immediateoutcomes – the authors used the equivalent toan F-test provided with the NLTS2 restricteduse data from the IES and SRI International todetermine statistically significant differencesbetween groups. The F-test equivalent, con-ducted within Excel, allowed the authors toinput the frequencies and standard errors for

two groups and determine a statistically signif-icant difference. Use of the F-test equivalentassessment allowed the researchers to com-pare if the variances in two populations wereequal. For the purposes of this secondary anal-ysis, differences were compared between out-comes for students with ASD within two yearsand four years, within two years and six yearsand within four years and six years. The fol-lowing post-school outcomes between theyears were compared: ever paid job, currentpaid job, independent living, attended commu-nity college, attended four-year college and at-tended vocational/technical school.

Results

The majority of student with ASD who wereout of school were out because they graduated(87.2%, SE 3.2). Others were out because theydropped out or stopped going (7.5% SE 2.6),some reason not specifically asked in the sur-vey (3.0% SE 1.0) (i.e., took test to receivediploma/certificate, dropped out or stoppedout, permanently expelled, or graduated) ortook a test to receive a diploma or certificate(1.6% SE 1.2).

Post-Secondary Education

Between one-fourth and one-third of studentswith ASD attended some form of post-second-ary education within two, within four (two-to-four), and within six (four-to-six) years afterexiting high school: 27.5% (SE 6.8), 30.6%(SE 7.4), and 29.2% (SE 6.9%), respectively.Of the post-secondary education options, themost frequently reported was attending acommunity college (see Table 1). Attendanceat community colleges experienced an in-crease in frequency the longer students wereout of school 23.3% (SE 5.8) of students withASD attended within six years, 20.0% (SE 4.9)within four years, and 15.1% (SE 4.4) withintwo years out of school. Attendance at four-year institutions also increased the longer stu-dents with ASD was out of school (i.e., 15.8%,SE 4.8 within six years of high school). Incontrast, a decreasing percentage of studentsattended vocational/technical school thelonger they were of school; the highest fre-quency was within two years of high school(10.2%, SE 4.0) and decreasing to 5.7% (SE


2.6) within six years of high school exit. Notsurprisingly, the longer one was out of highschool, the higher the frequency that thosewho attended earned a diploma (refer to Ta-ble 1). The highest frequency of students withASD earned a diploma from postsecondaryeducation institute when out of high schoolwithin six years.

Employment

Students with ASD reported an increased fre-quency of being both currently employed andever employed within the wave of data collec-tion the longer they were out of school (referto Table 1). While only 17.1% (SE 4.1) and30.4% (SE 5.5) were currently at the time ofdata collection or ever employed within thewave of data collection within two years ofexiting high school, 52.6% (SE 10.7) and84.7% (SE 4.4) were, respectively, within sixyears after high school. However, there was adecrease in the frequency of being currentlyemployed between within four years andwithin six years (63.9%, SE 9.7 and 52.6%, SE10.7, respectively). In contrast to the generalpositive trend of increasing employment thelonger one was out of school, a decrease oc-curred in frequencies the longer one was out

of school for both working full time as well asearning more than a minimum wave (see Ta-ble 2). The mean for minimum wage was$6.66 for within two years of exiting school,$5.46 for within two-to-four years of exitingschool, and $5.26 for within six years of exit-ing school.

Students generally reported liking their em-ployment very much or fairly well across thepost-school times examined. The longer indi-viduals were out of school, however, the morethey reported liking their job (refer to Table2). Yet, in contrast, the longer students wereout of school the less likely they were to findtheir own job – decreasing from 25.6% (SE6.3) during the immediate outcomes to 15.8%(SE 6.6) and 5.2% (SE 2.4) with more long-term outcomes. Students increasingly drovethemselves to work or rode with family the lon-ger they were out school.

Independent Living

A relatively small percentage of students withASD reporting living independently – definedin this secondary analysis as living on one’sown, living with a spouse or roommate, orliving in a college or military dorm – through-out the post-school years examined. The high-

TABLE 1

Post-school Outcomes: Postsecondary Education, Employment, and Independent Living

AttendCC

DiplomaCC

AttendVoc

DiplomaVoc

Attend4-year

Diploma4-year

PaidJob

CurrentPaid Job

Ind.Living

Within 2 YearsYes 15.1 — 10.2 — 7.2 0 30.4 17.1 3.1(SE) (4.4) (4.0) (2.4) (5.5) (4.1) (1.7)N 4360 576 4400 432 4368 274 3268 3268 3737

Within 4 YearsYes 20.0 19.4 6.6 0 10.8 0 46.1 63.9 23.4(SE) (4.9) (13.7) (2.0) (4.1) (12.4) (9.7) (5.2)N 4307 815 4288 224 4307 441 4006 4201 4318

Within 6 YearsYes 23.3 36.1 5.7 30.9 15.8 49.7 84.7 52.6 4.4(SE) (5.8) (10.9) (2.6) (12.9) (4.8) (15.1) (4.4) (10.7) (1.6)N 4303 1014 4260 328 4260 653 4240 4229 4303

Note: All percentages are based on the number of weighted responses; not all individuals responded to eachquestion. The percent of students who received a diploma is based on the number who both indicated theyattended and who responded to the question. Dashes represent that an insufficient raw numbers of casesexisted to report the data, consistent with the specification of the IES.

ASD Outcomes / 257

est rate of independent living occurred whenstudents were within four years of exiting highschool (23.4%, SE 5.2); the frequencies forwithin two years and within six years were bothless than 5%.

Differences in Outcomes across Time

When analyzing for statistically significantdifferences in the frequencies of outcomesacross the three points in time, none werefound for attending any of the three post-secondary education options. Statistically sig-nificant differences were found for having apaid job any time within the time frame andfor currently having a paid job. Statisticallysignificant differences were found for having apaid job at some point during the two-yearwave when comparing outcomes within twoyears of exiting school and within six yearsof exiting school (F � 59.42, p � .001) andwithin four years of exiting school and withinsix years of exiting school (F � 8.61, p � .01).Statistically significant differences were alsofound for students with ASD with regards tohaving a job at the time of data collectionwhen comparing the frequencies for both

within two years and within four years (F �19.75, p � .001) and for within two years andwithin six years (F � 9.60, p � .01). Finally, thefrequencies for living independently were sta-tistically significantly different for both withintwo years and within four years (F � 13.77, p �.001) and within four years and within sixyears (F � 12.2, p � .001).

Discussion

This study represented a secondary analysis ofthe NLTS2 aimed at understanding the imme-diate and long-term post-school outcomesof students with ASD and the relationshipamong those outcomes. The findings suggestthe longer an individual with ASD is out ofhigh school the more likely s/he is to bothattend and then graduate from most all formsof post-secondary education. The same is alsogenerally true for having a paid job – both atthe time of data collection and ever within thetwo-year window period. However, the resultsfor independent living, earning more thanminimum wage, and working full-time tell adifferent – and more confusing – story forindividuals with ASD.

TABLE 2

Post-school Outcomes: Aspects of Employment

HourlyWage

FullTimeWork

Likes Work:Very Much/Fairly Well

Foundjob:Self

Gets to Work

Drivesself

Ridewith family

PublicTrans

Walks orRides Bike

Within 2 Years% 83.2 24.7 65.7 25.6 27.1 28.1 7.5 16.0(SE) 7.5 (6.1) 10.5 (6.3) (8.7) (5.0) (2.3) (6.4)N 976 951 571 924 1144

Within 4 Years% 37.9 8.8 100 15.8 22.8 13.1 31.5 —(SE) 12.2 (3.0) (6.6) (7.7) (4.5) (9.6)N 2779 2837 769 2822 2868

Within 6 Years% 32.2 9.6 84.0 5.2 43.4 32.7 — —(SE) 10.6 (3.2) 5.3 (2.4) (16.2) (10.0)N 2739 3333 856 3198 471

Note: All percentages are based on the number of weighted responses; not all individuals responded to eachquestion. The percent of students who received a diploma is based on the number who both indicated theyattended and who responded to the question. Dashes represent that an insufficient raw numbers of casesexisted to report the data, consistent with the specification of the IES.


From a positive perspective, students withASD were employed at the time of data collec-tion at a rate of over 50% when out of schoolmore than two years. However, the discrep-ancy between being ever employed through-out the time frame examined and being cur-rently employed is concerning. For example,84.7% of adults with ASD reported having ajob at some point within six years after highschool, yet only 52.6% were currently em-ployed. These results suggest the need tomore critically examine post-school servicesprovided to individuals with ASD relative toemployment. They also suggest value in sec-ondary schools collaborating earlier and to agreater extent with post-school agencies, suchas vocational rehabilitation (Wilczynski, Tram-mell, & Clarke, 2013). Perhaps adults withASD need additional support in keeping a job,outside of just getting a job. Shattuck et al.(2012) suggested a key element in supportingstudents with ASD with employment was tocarefully match work experiences to an indi-vidual’s area of strength. Also, it is also impor-tant to ensure that students with ASD leavehigh school with as many soft vocational skills,such as social skills, as possible, given the im-portance of these skills in obtaining and main-taining employment (Wilczynski et al., 2013).When these services are not provided or skillsare not obtained in high school, it becomescritical that individuals with ASD get access toservices and then acquire the services asadults, such as through vocational rehabilita-tion and/or a state’s Developmental DisabilityAgency (Simonsen & Neubert, 2013).

Also positive were the increasing number ofindividuals with ASD who attended a commu-nity college or a four-year institution the lon-ger they were out of school, and the increas-ing frequency of individuals who earned adiploma from the institutions they attended.While one could suggest that rates of post-secondary education participation were lowfor adults with ASD, they were actually higherthan other populations (see Table 3 for acomparison of some outcomes for studentswith ASD to other individuals with disabili-ties). Frequencies of postsecondary educationattendance of 27.5% for within two years ofhigh school exit, 30.6% for within four years,and 29.2% for within six years is higher thanconsistently reported with previous research

(Shattuck et al., 2012; Wehman et al., 2014).However, the results suggest perhaps the needfor additional supports to help adults withASD complete their postsecondary education.

It is important to note that much of thepostsecondary education data occurred priorto the increase in postsecondary educationoptions and programs for individuals with dis-abilities, and specifically students with ASD. Asevident on ThinkCollege (2016), there arenow postsecondary education programs tosupport students with ASD specifically. Theseprograms are designed to provide individualswith ASD opportunities to participate in post-secondary education outside of the traditionaldisability services offered on college campusesto any individuals with a disability followingSection 504 of the Rehabilitation Act of 1973and the Americans with Disabilities Act of1990. The programs highlighted on Think-College provide unique supports and servicesto students with ASD for whom education be-yond high school might not be possible or pres-ents large challenges (Hendrickson, Carson,Woods-Goves, Mendenhall, & Scheidecker,2013). For example, postsecondary educationprograms targeting students with ASD often in-clude specific supports focused on the socialneeds – including socialization and navigatingsocial environment – of college of students withASD as well as daily living skills (Cullen, 2015).Social communication challenges and supportsare often noted as a major issue for individualswith ASD in terms of accessing and findingsuccess in postsecondary educational set-tings (Zager & Alpern, 2010).

As one might predict, the rates of earning adiploma from a postsecondary institution in-creased the longer an individual with ASD wasout of school. Yet, less than 50% of studentswho attended an institution of postsecondaryeducation earned a diploma within six years.Additional supports and services for studentswith ASD should be investigated for both highschool as well as post-secondary to increasestudent opportunities for the ultimate successof graduating. It is insufficient to attend; stu-dents need to earn a diploma – or certificate –from postsecondary education institutions. Itis also imperative that as models for collegeattendance – like those on ThinkCollege(2016) – increase postsecondary education ac-cess to more individuals with ASD that data

ASD Outcomes / 259

are gathered to understand the impact of suchprograms (Hendrickson et al., 2013; Zeedyk,Tipton, & Blacher, 2014). Data released onspecialized college programs for students withASD – and/or students with intellectual dis-ability – to date suggest positive implicationsfor such programs, including rates of employ-ment and independent living (Ross, Marcell,Williams, & Carlson, 2013).

From this secondary analysis, outcomes forindividuals with ASD were obtained with re-gards to earning more than minimum wageand working full time the longer one was outof school. These were true both for individualswith ASD as well as in comparison to individ-uals with other disabilities (refer to Table 3).Although disappointing and perhaps not fullyreflective of the skills of individuals with ASD,the tendency towards part-time employment isnot inconsistent with previous research forindividuals with ASD (Holwerda, van derKlink, Groothoff, & Brouwer, 2012). It is alsoimportant to evaluate the data in light of con-text. For example, data for within six years of

exiting high school was collected during 2006and 2007. In the United States, 2006 and 2007marked the beginning of a slowdown andeventual recession in the economy (Koba,2011; Weller, 2006). If 2006 was marked by aneconomic slowdown, individuals with disabili-ties may have experienced a decreased inhours employed or accepted jobs with lowerpay or obtained through other means, such assupported employment or even in shelteredworkshops. Employment in sheltered work-shops is associated with lower wages earned aswell as working fewer hours (Cimera, 2011;Cimera, Wehman, West, & Burgess, 2012). Ofcourse, another hypothesis outside of the im-pact of the economic slowdown is that theindividuals who remained longer in the longi-tudinal study were qualitatively different thanthose who left the study. In other words, attri-tion was a factor and students for whom dataare available during wave 4 – within six yearsof school exit – are different in key qualitiesthat contribute to employment, such as IQ,service needs, functional and/or communica-

TABLE 3

Comparison of Some Outcomes for Students with ASD to Other Students

Students withASD

Students withOther Disabilities

Students withoutDisabilities

Students with MID(Bouck, 2014)

Currently employed (within2 years or shortly afterleaving school)

17.7% 42.9% (Wagneret al., 2005)

79.6% (Bureau ofLabor Statistics,2012)

36.7%

Currently employed (within4 years)

63.9% 56.8% (Newmanet al., 2009)

48.3%

Full/part time employment(within 4 years)

8.8% 57.9% (Newmanet al., 2009)

53.9%

Hourly wage (within4 years)

$5.46 $8.20 (Newmanet al., 2009)

$9.20 (U.S.Department ofLabor, 2001)

$7.35

4-year post-secondaryattendance (within4 years)

10.8% 45% (Newmanet al., 2009)

61.1% (IES,NCES, n.d.)

3.0%

Post-secondary attendance 27.5% (within2 years)

19.7% (Wagneret al., 2005)

19.2% (U.S.Department ofCommerce,2011)

17.1% (within2 years)

Independent living 3.7% (within2 years)23.4%(within4 years)

24.7%(Newmanet al., 2009)

28% (Arnett,1998)

30.9% (within2 years)17.5%(within4 years)


tion needs, and family support (Holwerda etal., 2012; Roux et al., 2013). A final hypothesisis that during the years examined the fullextent of support was not available. For exam-ple, as previously noted researchers are find-ing positive results for individuals with ASDengaged in specialized college programs thatprovide additional education and supports forindividuals with ASD and/or intellectual dis-ability after high school, including increasedrates of employment (Ross et al., 2013).

Finally, the lower rates of independent liv-ing for individuals with ASD are consistentwith previous research regarding the residen-tial status of this population after high school(Anderson et al., 2014; Newman et al., 2011).Perhaps concerning with these data is the lackof apparent improvement from living statuswithin two years of high school exit to withinsix years of high school exit. Despite the hy-pothesis that one’s outcomes – including in-dependent living – would improve the longerone was out of school, these results were notconsistently obtained for adults with ASD. Asprevious discussed, perhaps the individualsfor whom the longest amount of data werecollected represent individuals with moreneeds, and adults with ASD with more positiveoutcomes were lost due to the attrition withinthe original study. It could also be that thedecrease in independent living within fouryears – in which an increase occurred fromwithin two years – and within six years of beingout of school reflect the changing societaleconomic times previously mentioned with re-gards to a downturn in full time employmentand wage earnings. Like previously suggestedfor employment and post-secondary educa-tion, additional research regarding the factorscontributing to the differing rates of post-school success is warranted. Also warranted isadditional attention in practice to preparinghigh school students and adults with ASD, viarehabilitation services, for independent livingand trying to provide the prerequisite skills forsuccess (Anderson et al., 2014).

Limitations

This secondary data analysis of the NLTS2 issubject to the same limitations of the originalNLTS2 data. Thus, there exist concerns aboutmissing data from individuals not being asked

and/or not responding to every question. Inaddition, attribution occurred within the orig-inal NLTS2 data collection. Related, given thesecondary nature of the data, researcherswere only able to analyze the existing datafrom the NLTS2 or what could be recodedfrom the original data. Hence, particular nu-ances of post-school outcomes might be miss-ing given the lack of original data collection.Limitations also exist relative to the originaldata collection. For example, for the variableemployment the surveys inquired about paidemployment, but did not differentiate be-tween integrated employment or employmentin a sheltered workshop. Finally, the NLTS2data are based on self-report – by the student,parent, or educator – and self-reported datacan be biased.

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Effects of Self-Mediated Video Modeling Compared to VideoSelf-Prompting for Adolescents with Intellectual Disability

Sally B. ShepleyThe University of Kentucky

Kevin M. Ayres, Rachel Cagliani, andErinn Whiteside

The University of Georgia

Abstract: Self-instruction is a pivotal skill that promotes independence and self-determination by allowingindividuals to independently access prompts during the acquisition of new skills while decreasing the need forsupport from another person. Self-mediated video modeling (SMVM) and video self-prompting (VSP) are twomethods of video-based self-instruction in which individuals use technology to access visual support whilepracticing new skills. This study evaluated the efficiency of SMVM versus VSP to teach four high school femaleswith intellectual disability art-related tasks in a school setting. An adapted alternating treatments design wasused to compare the effects of SMVM and VSP on sessions to criterion and instructional time to criterion. Resultsindicated that both SMVM and VSP resulted in an increase in percentage of correct responses across allparticipants, but VSP tasks resulted in all participants reaching mastery criterion in the fewest sessions tocriterion.

Self-instruction occurs when an individualuses resources available to learn a skill ratherthan relying on support or guidance provideddirectly by another person. The materials usedfor self-instruction may serve as the promptto notify the learner of the step that shouldoccur next in a sequence to complete a skill(Browder & Shapiro, 1985). Self-instructionpromotes monitoring of one’s own behaviorswhen completing a task and is a form of self-directed learning. Teaching individuals howto self-instruct fosters self-determination (Agran &Wehmeyer, 2000). Self-determination is theability to make decisions on one’s own life andplays a role in the success of an individualafter graduating high school (Wehmeyer &Palmer, 2003). Self-instruction is a pivotal skillin that it allows individuals to not only learnone to three target behaviors, but instead, canpotentially have collateral effects on severalbehaviors if the self-instruction skills gener-alize (Smith, Shepley, Alexander, & Ayres,2015). For instance, once an individual learns

how to self-instruct while learning to make aham and cheese sandwich, they have acquiredtwo skills (making a sandwich and self-instruc-tion). While making a ham and cheese sand-wich is valuable in a particular context, theskill of using a set of procedures to self-in-struct creates opportunities to learn new skills.

Individuals with intellectual disability (ID)rely on teachers and other postsecondary in-structors, such as job coaches, to predict andteach skills needed for obtaining and main-taining employment and for independent liv-ing. Teaching each skill in isolation that isrequired for future employment or commu-nity settings is an inefficient use of time andresources. Further, teaching all skills neededto fully integrate into one’s community andlive independently is impossible. Instructorsand teachers might consider teaching individ-uals how to self-instruct rather than teachingskills in isolation to maximize instruction timeand increase independent skills (Smith et al.,2015). With web-based resources like You-Tube, engaging in self-instruction has becomemore commonplace. For instance, if oneneeds to change a battery in a car, learn to fixa dishwasher, or even play an instrument, theycan find instructional materials for free onYouTube. The challenge instructionally then

Correspondence concerning this article should beaddressed to Sally B. Shepley, The University of Ken-tucky, Early Childhood, Special Education, and Reha-bilitation Counseling, 229 Taylor Education Building,Lexington, KY 40506. E-mail: [email protected]




is to teach individuals with ID to sift throughthe available instructional resources to iden-tify a prompt corresponding to their needs.

Learning to self-instruct has the dual bene-fit of decreasing reliance on others while al-lowing a learner to pursue those topics ofgreatest need and interest to him or herself.Further, self-instruction may result in less stig-matization because rather than a teacher orjob coach accompanying an individual intothe natural environment, the learner can usemobile technology to self-instruct. Addition-ally, because self-instruction reduces depen-dence on instructors, instructors may havemore time to dedicate to working with otherlearners or completing other duties.

Smith et al. (2015) reviewed the researchon self-instruction strategies for individualswith ID that specifically resulted in generaliza-tion to novel skills. The results indicated that56 of 57 included participants in the reviewlearned at least one multi-step skill with aself-instructional strategy, “meaning the par-ticipant independently controlled and manip-ulated the [self-instruction] materials” (p. 21).The review identified three times within theexperimental process in which individualslearned to self-instruct, including the use ofhistory training prior to the baseline condi-tion, teaching between baseline and inter-vention, or teaching during the interventionitself. Of the 56 individuals with ID that suc-cessfully acquired a self-instructional strategy,only 32 generalized this strategy to learn atleast one additional multi-step skill. Smith etal. identified potential barriers related to gen-eralization of self-instructional skills in the in-cluded studies. One example barrier was 7 ofthe 19 studies incorporated vocal directionsto use the self-instruction tool in their taskdirection (e.g., “Restock the vending ma-chine. Watch the video on your iPod”). Fun-damentally, this means the learners did nothave to discriminate on their own that theyneeded to use their self-instructional tools,rather they were directly coached by an in-structor to do so. This, in some ways, defeatsthe end goal of self-instruction. Another issuethat Smith et al. (2015) cited related to a lackof instruction for learners on how to searchfor and select self-instructional materials. In18 of the 19 included studies, researchers di-rectly loaded the self-instructional materials to

the specific prompt required for task comple-tion (e.g., the audio tape corresponding totarget task was loaded in the cassette player ora video model demonstrating the targetedskill was loaded to a mobile device). An alter-native would be teaching an individual to nav-igate through their self-instructional tool tolocate a specific prompt among multiple avail-able options. As technology has advanced, self-instructional tools have the potential to storemultiple needed prompts to complete identi-fied targeted skills.

The tools used by individuals with ID toself-instruct have evolved since the 1980s whenresearchers used books and picture promptsto teach a novel set of skills (e.g., Wacker &Berg, 1983, 1984). Self-instruction later incor-porated more high-tech tools, such as cassetteplayers with headphones to provide auditoryprompting in the late 1990s (Trask-Tyler, Grossi,& Heward, 1994), and portable DVD playersto promote self-meditated video modeling(SMVM) in the early 2000s (Mechling, Gast, &Fields, 2008; Mechling & Stephens, 2009). Be-ginning in 2009, handheld devices becamemore prevalent on the technology market;therefore, the tools used in self-instructional re-search followed suit. Mechling, Gast, and Seid(2009) taught participants SMVM using aHewlett Packard iPAQ Pocket PC to completecooking recipes. The first-generation AppleiPhone was released in 2007 (Apple Inc., 2007)and Bereznak, Ayres, Mechling, and Alexander(2012) first used it as a self-instructional tool forlearners with ID and autism spectrum disorder(ASD) to complete vocational and independentliving tasks.

Additional research has focused on videoprompting self-instruction, or video self-prompting (VSP), in which a video of a task isseparated into steps so that the individualviews and then imitates a single step or a fewsteps of the task at a time, as opposed to theentire task in video modeling. Bereznak et al.(2012) evaluated the use VSP and taughtthree high school students how to pause andplay video prompts of vocational and dailyliving skills such as using a washing machine,making noodles, and using a copy machine.After each step in the task analysis, the videodisplayed a stop sign for 4 s signaling to theparticipant to pause the video and completethe demonstrated step. Two of the three par-

Self-Instruction Video Comparison / 265

ticipants learned to pause and play the videoprompts and reached mastery criterion of thevarious vocational and daily living tasks.Shepley, Spriggs, Samudre, and Elliott (2017)taught four middle school students with ID toself-instruct using a similar VSP format. Theresearchers created videos with embeddedpause signs [similar to Bereznak et al.’s (2012)stop signs] to cue the participants to pause thevideo, complete the demonstrated step(s),and resume play to view additional videoprompts. All participants learned to navigatethe technology to find the necessary videosand pause/play videos using a system of leastprompts instruction. After completing tech-nology training, three of the four participantswere able to self-instruct using video promptsto acquire a novel daily living task sequence(i.e., set the table, make noodles, and make acup of punch). These studies provided sup-port for video prompting as an effective self-instructional tool for learners of various ageswith ID.

As mentioned above and represented in thepublished video-based instruction literature,videos can take one of two forms, video mod-eling and video prompting. Both methodshave led to skill acquisition, yet it is importantto assess efficiency of instruction to ensureinstructional time is spent wisely, thus maxi-mize outcomes for learners with ID. Mechling,Ayres, Bryant, and Foster (2014) comparedVM and VP, along with continuous VM, inwhich the video looped until the researcherstopped the video. For all three forms of videoinstruction, the researcher delivered compo-nents of the self-instruction task analysis (e.g.,setup the technology, pressed play on the vid-eos); therefore, the participants did not fullyself-instruct. Mechling et al. (2014) found thatVP was most efficient (i.e., sessions, time, anderrors to criterion) when acquiring chainedtasks in which each step in the task analyseswas completed only once. Taber-Doughty etal. (2011) compared the two self-instructionstrategies, SMVM and VSP, in conjunctionwith system of least prompts instruction toteach cooking skills to middle school studentswith ID. The authors concluded that videomodeling was more effective for two of thethree included students and video promptingwas more effective for the third participant.Given that researchers used system of least

prompts instruction in conjunction with vid-eo-based instruction and sessions were notconducted to mastery criterion, additional re-search is needed to determine which methodin isolation is more efficient.

Two forms of video-based self-instructioninclude video modeling and video prompting.Both strategies incorporate videos but are dif-ferent in the presentation of the videos. Videoprompting task analyzes the various steps of askill and segments the video so that each clipserves as a prompt to complete that step of theskill. Video modeling displayed the entire tar-geted tasks as one video prior to allowing thelearner to imitate the observed steps. Whilethese various forms of self-instruction are botheffective interventions (i.e., have resulted inacquisition of novel tasks) more informationis necessary to determine the most efficientself-instructional method for individuals withID. The purpose of this study was to comparethe efficiency of self-instruction with mobiletechnology when presented as a SMVM andwhen presented as VSP. The research ques-tion was: Will self-instruction using SMVM orVSP result in more efficient instruction (i.e.,rate of acquisition) for participants with ID?

Method

Participants and Setting

Four high school females ages 15 to 20 yearsold participated in the study. All participantsattended a rural public high school and re-ceived daily instruction in a special educationclassroom. Through school eligibility, oneparticipant was identified as having moderateID (Meg), two with mild ID (Jo and Amy), andone with ASD and mild ID (Beth). Specificparticipant demographic information is lo-cated in Table 1. All participants demon-strated the prerequisite skills of attending to atask for 10 min, imitating a video model, re-ceptively discriminating between five pictures,and fine motor skills that allowed navigationof an iPhone as well as the fine motor skillsrequired for the target tasks. Additionally, allparticipants had previous experience with mo-bile technologies, and three had their owndevices (e.g., iPod Touch). The study tookplace in a teacher workroom down the hallfrom the participants’ special education class-


room. Sessions were conducted in a one-to-one format at a table with four chairs. Doctoral-level special education students ran all sessionsand collected data for each session.

Materials

Instructional materials. The researchers usedtwo iPhone 4s loaded with video models andvideo prompts of the target tasks to providevideo-based instruction to the participants.Videos were filmed from a performer’s pointof view (Ayres & Langone, 2007) depictingtwo hands completing the target origamitasks, providing the same perspective of thetask that participants would see as they com-plete the tasks themselves. Origami tasks wereused to increase internal validity by reducingthe likelihood that participants had previousexposure to the tasks and to equate task diffi-culty for precise comparison. Voice narrationverbalizing the steps within the task analyseswere added. Videos were uploaded to theiPhone and stored under the Videos applica-tion which was located in the top right cornerof the iPhone home screen.

Task materials. Task materials for eachcondition consisted of a 15 cm � 15 cm ori-gami paper (i.e., green for tree, red for heart,brown for cup). Data collection sheets wereused in each session that allowed data collec-tors to track percentage of correct responses,sessions to criterion, instructional time to cri-terion, and procedural fidelity.

Technology Training

Following baseline sessions and prior to begin-ning the comparison condition, all partici-pants were taught to initiate self-instruction bypulling the iPhone out of their pocket, navi-gate to the video corresponding to the taskdirection, view the video, and press pause/play if the screen displayed a pause sign. Vid-eos and tasks used in technology training weredifferent than those used in the experimentaldesign. The training tasks involved nonsenseand unpredictable folds in origami paper,meaning the end product did not results in anidentifiable object or animal like traditionalorigami. These nonsense tasks were labeledby the color of the paper (e.g., “Make theorigami purple shape”) to ensure the partici-pant needed to self-instruct. These self-in-structional steps were taught using a system ofleast prompts procedure in which the re-searcher provided a verbal prompt (e.g.,“Press the videos icon”), followed by a gesture(e.g., pointing to the videos icons) if incorrector no response, followed by a physical prompt(e.g., guiding the participant’s finger to pressthe videos icon) to ensure correct responding.Training was conducted until each participantperformed two consecutive sessions at 100%independent correct responding for phonenavigation steps for a SMVM task and for aVSP task, and then again for two consecutivesessions at 100% for a second novel SMVMand VSP task. See Table 2 for technologytraining data.

TABLE 1

Participant Information

Participant Age* School Eligibility IQ Adaptive Behavior Scale Autism Rating Scale

Meg 20.3 MoID 42a 60b NAJo 15.1 MID; SLI 44a 69c NABeth 18.0 ASD; MID; SLI 74d 59c Very elevated rangee

Amy 16.3 MID; SLI 50f 66c NA

Note: *Based on the start of the study. a Differential Abilities Scales: Second Edition (Elliot, 2007);b Vineland Adaptive Behavior Scales – Interview Edition: Second Edition (Sparrow, Cicchetti, & Balla, 2005);c Adaptive Behavior Assessment System: Second Edition (Harrison & Oakland, 2003); d Wechsler AdultIntelligence Scales: Fourth Edition (Wechsler, 2003); e Autism Spectrum Rating Scales (Goldstein & Naglieri,2009); f Stanford-Binet Intelligence Scales: Fifth Edition (Roid, 2003); ASD � Autism Spectrum Disorder;MID � Mild intellectual disability; MoID � Moderate intellectual disability; SLI � Speech language impair-ment; NA � not applicable.


Dependent Measures

Data were collected on three dependent vari-ables: percentage of correct responses, ses-sions to criterion, and instructional time tocriterion. For a response to be scored as cor-rect, it had to be a topographically accurateresponse according to the task analysis, beinitiated within 5 s of a discriminative stimu-lus, and completed within 15 s. Incorrect re-sponses occurred due to latency errors (notinitiating the step within 5 s), duration errors(not completing the step within 15 s), or to-pographical errors (not engaging in the cor-rect action to complete the step). The numberof correct responses was divided by the totalnumber of responses and multiplied by 100 toobtain the percentage of correct responsesper session (see Table 3 for task analyses ofthe targeted tasks). Sessions to criterion con-sisted of the number of instructional sessionsrequired for a participant to reach 100% mas-tery per task. Instructional time to criterionwas defined as the total time required duringall instructional sessions for a given treatment.This was calculated by starting a stop watch

after delivery of each task direction and stop-ping upon completion of the final step in each

TABLE 2

Technology Training Sessions to Criterion andTime in Instruction

SMVM VSP

Sessions toCriterion

Sessions toCriterion

Total Durationin Instruction

Total Durationin Instruction

Task 1 Task 2 Task 1 Task 2

Meg 5 2 3 26.25 m 2.48 m 5.07 m 2.98 m

Jo 3 2 8 25.52 m 3.12 m 18.17 m 5.35 m

Beth 3 2 4 34.92 m 2.15 m 6.42 m 4.07 m

Amy 4 2 4 28.35 m 2.52 m 7.25 m 4.42 m

Mean 3.75 2 4.75 2.256.26 m 2.57 m 10.16 m 4.20 m

Note: SMVM � Self-mediated video modeling;VSP � Video self-prompting; m � minutes.

TABLE 3

Task Analyses for Origami Tasks

Task Task Steps

Cup 1. Fold one corner to opposite corner withbrown side showing

2. Turn paper so point is at top3. Fold top point of top paper down to flat edge4. Unfold step 35. Fold left corner over to meet right side at

crease6. Fold right corner over to meet left side7. Fold top flap down along horizontal line8. Unfold step 79. Make an opening in cup and fold top flap

into opening10. Fold top point down over cup11. Unfold step 1012. Make an opening in cup top and fold flap

into cup opening13. Use fingers to open cup

Tree 1. Fold one corner to opposite corner withgreen side showing

2. Unfold step 13. Fold right point along mid-line4. Fold left point along mid-line5. Flip paper over6. Fold right point along mid-line7. Fold left point along mid-line8. Flip paper over9. Fold bottom point up to top point

10. Flip paper over11. Fold top point down to edge so it hangs over

around 1.5 inches12. Fold bottom point up a little bit13. Flip paper over

Heart 1. Fold one corner to opposite corner with redside showing

2. Turn paper so point is at top3. Fold right corner up to top point4. Fold left corner up to top point5. Flip paper over6. Fold top two flaps down to bottom point7. Fold top right point to right corner along

midline8. Fold top left point to left corner along

midline9. Unfold steps 7 and 8

10. Open top right flap and fold in alongmid-horizontal line

11. Open top left flap and fold in alongmid-horizontal line

12. Fold top right point down13. Fold top left point down14. Fold in right corner a little bit15. Fold in right top point a little bit16. Fold in left corner a little bit17. Fold in left point a little bit18. Fold bottom two flaps up to center19. Flip over and prop up heart


task analysis. The duration for each task wassummarized to report total time in instructionwith each independent variable.

A secondary observer collected inter-ob-server agreement (IOA) data and proceduralfidelity (PF) data for a minimum of 20% ofsessions in all conditions for each treatment.Inter-observer agreement was calculated usingpoint-by-point agreement in which the num-ber of agreements were divided by the num-ber of agreements plus disagreements andmultiplied by 100 (Ayres & Ledford, 2014)resulting in 100% IOA for all sessions. Proce-dural fidelity was collected on the followingresearcher behaviors: (a) correct responses re-ceived praised on a CRF schedule of reinforce-ment, (b) incorrect responses were correctedusing a multiple opportunity probe, (c) thephone was in the participant’s pocket 3 to 10min before sessions, (d) the correct task direc-tion was delivered, (e) the correct origamipaper was handed to the participant, (f) thetimer was started following the task directionand stopped upon completion of the finalstep, and (g) researcher implemented multi-ple opportunity probe correctly includingadhering to latency and duration time re-straints and correcting incorrect steps out ofview of the participant. PF was calculated bydividing the number of observed researchbehaviors by the number of planned re-searcher behaviors and multiplying by 100(Gast, 2014). Mean PF was 99.8% across allsessions with a range of 97% to 100%.

Experimental Design

An adapted alternating treatments design(Sindelar, Rosenberg, & Wilson, 1985; Wolery,Gast, & Ledford, 2014) was used to comparethe effects of SMVM to VSP. The design in-cluded a baseline phase to first establish stu-dent performance on the origami tasks for aminimum of three sessions or until stable (i.e.,a decelerating or zero-celerating trend in thedata path). The comparison condition of thestudy was initiated in which participants wereasked to fold each of the three different ori-gami figures. The SMVM and VSP tasks werecounterbalanced across students, and thesame task (i.e., origami heart) was used for thecontrol set for all participants. Each partici-pant was only exposed to one treatment per

origami figure. The control condition wasused to allow for an opportunity to replicatethe demonstration of the effect of the moreefficient instructional procedure and providea more compelling case for a functional rela-tion. The sequence (SMVM, VSP, and con-trol) of tasks was randomized each day and allthree sessions were conducted each day. Ses-sions were conducted three to five days a weekwith no more than one session for each taskconducted a day. Following at least six sessionsand the acquisition of at least one origamifigure (100% of steps performed correctlywith the instructional procedures), the com-parison condition stopped and the replicationcondition began. In the replication condition,the treatment that resulted in the most rapidacquisition (based on the number of trainingsessions) was then applied to the control task,allowing for an opportunity to replicate theacquisition effects. If one of the tasks in theinitial comparison failed to reach mastery cri-terion, the participant received additional in-struction on that task with the treatment thatwas most effective (i.e., best treatment condi-tion).

Procedure

General procedures. Three to ten min priorto running a session, the researcher gave theparticipant an iPhone, loaded with videos cor-responding to that condition and participant,and asked them to place the phone in theirpocket. This was done to replicate how thegeneral population begins to self-instruct(Smith et al., 2016). Once seated at the tablein the teacher workroom, the researcher de-livered the task direction to (e.g., “Make anorigami cup, use your phone if you want to”)while handing the participant the origami pa-per that corresponded with the task direction.For each task, participants were given 5 s toinitiate, and if initiated, 15 s duration to com-plete each step. If participants failed to initiateor responded incorrectly, the researcher per-formed the step out of view of the participantand represented the paper to the participantso the following step could be performed (i.e.,multiple opportunity probe; Cooper, Heron,& Heward, 2007). This continued until eitherthe participant or the researcher completedthe final step for each task. All correct steps


received general verbal praise (e.g., “Nicejob”).

Baseline. Sessions followed general proce-dures. During baseline conditions, the videosloaded on the iPhone restated the task direc-tion and showed a picture of the final prod-uct, but did not play any step by step videos tocomplete the targeted tasks.

Comparison. All sessions followed generalprocedures. During SMVM sessions, a videodepicting the entire task, start to finish, wasloaded in the iPhone. While watching thevideo model, attempts to fold along with thevideo were blocked in order to ensure VM wasimplemented as described in the literature, asopposed to simultaneous VM in which alearner imitates a video model while it is play-ing (Sancho, Sidener, Reeve, & Sidener, 2010;Taber-Doughty, Patton, & Brennan, 2008). Toblock, the researcher placed a hand on theorigami paper until the video finished. DuringVSP sessions, videos included a 4 s pause signbetween each step in the task analyses. Thisred and white pause symbol was accompaniedby an audible “pause.” Attempts to fold alongwith the video were blocked by placing a handon the paper until the participant pressedpaused. If a participant continued to com-plete a step before viewing the correspondingvideo prompt, the researcher blocked this at-tempt by placing a hand on the origami paperand pointed to the phone. Control sessionswere identical to baseline procedures. Thecomparison condition was conducted for aminimum of six sessions and until a treatmentreached criterion of 100% correct respondingfor one session.

Replication. The treatment with the fewestsessions to criterion was applied to the controltask to evaluate for intrasubject replication oftreatment effects (Wolery et al., 2014). Thesame procedures were used as described in thecomparison condition for that specific treat-ment. Replication sessions were conducted for aminimum of three sessions and until the partic-ipant reached criterion of 100% correct re-sponding for one session.

Best treatment. If a participant did notreach criterion with both treatments duringthe comparison condition, the treatment withthe fewest sessions to criterion (i.e., the besttreatment) was applied to the task that did notreach criterion. This was conducted to assess if

applying the best treatment to a task withprevious intervention would produce thera-peutic changes in participant’s behavior. Thesame procedures were used as described inthe comparison condition for that specifictreatment. Best treatment sessions were con-ducted for a minimum of three sessions anduntil the participant reached criterion of100% correct responding for one session.

Results

Figures 1 and 2 demonstrate each partici-pants’ correct responding for the SMVM,VSP, and control tasks. Visual analysis of thedata revealed an increase in level for bothSMVM and VSP tasks for all participants. Ad-ditionally, all participants reached mastery cri-terion on the VSP task and one participantreach mastery on both the VSP and the SMVMtask. Overall, the fewest sessions to criterionfor all participants was the VSP task, making itthe best treatment based on the results of thisstudy.

Meg

During baseline, Meg performed 5% (range0–15%) of her SMVM task (origami tree),21% (range 15–23%) of her VSP task (origamicup), and 11% of her control task (origamiheart). During the comparison condition, sheperformed 44% (range 23–62%) SMVM task,84% (range 54–100%) VSP task, and 20%(range 5–26%) control task. She reached cri-terion on her VSP task in eight sessions andwas in VSP instruction for a total of 48.47 minuntil mastery, with each session lasting an av-erage of 4.91 min.

VSP was introduced to the control task inthe replication condition, resulting in an im-mediate change in level and an acceleratingtrend until reaching mastery in six sessions.Meg’s performance on the control task duringthe replication condition was 86% (range 74–100%) and lasted a total of 47.25 min (anaverage of 7.88 min per session). Lastly, VSPwas applied to the SMVM task in the besttreatment condition, resulting in 82% (range69–100%) correct responding and reachingmastery in five sessions.


Beth

During baseline, Beth performed 18% (range15–23%) of her SMVM task (origami tree),9% (range 8–15%) of her VSP task (origamicup), and 24% (range 21–32%) of her controltask (origami heart). During the comparisoncondition, she performed 87% (range 62–100%) SMVM task, 100% VSP task, and 32%(range 21–37%) control task. She reached cri-terion in four sessions using SMVW andreached criterion within one session usingVSP. Regarding total time spent in instructionuntil mastery, Beth was in SMVM instructionfor 18.47 min (an average of 4.62 min persession) and VSP for 5.82 min.

VSP was the more efficient instructionbased on sessions to criterion and thereforewas introduced to the control task in the rep-lication condition, resulting in an immediateand abrupt change in level, reaching masteryin four sessions lasting a total of 33.58 min (an

average of 8.40 min per session). Beth’s per-formance on the control task during the rep-lication condition was 93% (range 89–100%).

Jo

In baseline, Jo performed 21% (range 8–38%)of her SMVM task (origami cup), 27% (range15–38%) of her VSP task (origami tree), and13% (range 11–16%) of her control task (ori-gami heart). During the comparison condi-tion, she performed 79% (range 54–92%)SMVM task, 94% (range 77–100%) VSP task,and 41% (range 21–47%) control task. Shereached criterion with her VSP task in foursessions and was in VSP instruction to masteryfor a total of 26.52 min, with each sessionlasting an average of 6.63 min.

VSP was introduced to the control task inthe replication condition, resulting in an im-mediate change in level and an accelerating

Figure 1. Performance data for Meg and Beth. Closed triangles represent performance of SMVM task(origami tree). Closed circles represent performance of VSP task (origami cup). Open squaresrepresent performance of control task (origami heart).


trend until reaching mastery in four sessions.Jo’s performance on the control task duringthe replication condition was 88% (range 74–100%) and lasted a total of 37.22 min (anaverage of 9.30 min per session). Lastly, VSPwas applied to the SMVM task in the besttreatment condition, resulting in 97% (range92–100%) correct responding and reachingmastery in two sessions.

Amy

During baseline, Amy performed 5% (range0–8%) of her SMVM task (origami cup), 18%(range 8 –23%) of her VSP task (origamitree), and 21% (range 5–26%) of her controltask (origami heart). During the comparisoncondition, she performed 64% (range 54–77%%) SMVM task, 89% (range 77–100%)VSP task, and 43% (range 37–53%) controltask. She reached criterion with her VSP taskin five sessions and was in VSP instruction for

a total of 40.33 min, with each session lastingan average of 8.07 min.

VSP was introduced to the control task inthe replication condition, resulting in an im-mediate change in level and an acceleratingtrend until reaching mastery in six sessions.Amy’s performance on the control task duringthe replication condition was 89% (range 79–100%) and lasted a total of 64.17 min (anaverage of 10.69 min per session). Lastly, VSPwas applied to the SMVM task in the besttreatment condition, resulting in 86% (range77–100%) correct responding and reachingmastery in seven sessions.

Discussion

Overall VSP resulted in independent task per-formance for all participants and was themore efficient self-instructional tool. These re-sults are consistent with the literature compar-ing teacher-directed video modeling to video

Figure 2. Performance data for Jo and Amy. Closed triangles represent performance of SMVM task (origamicup). Closed circles represent performance of VSP task (origami tree). Open squares representperformance of control task (origami heart).


prompting (Cannella-Malone et al., 2011;Cannella-Malone et al., 2006; Mechling et al.,2014), in that video prompting was more effi-cient in terms of the number of sessions tocriterion. Few studies in the literature reportthe time required to training in self instruc-tion. While VSP may take more time to ini-tially train, (for example, in this study it took6 min for SMVM compared to 10 min forVSP); however, in the long term, the learnerwill acquire other novel skills in fewer sessionsthan if learned using SMVM. However, itshould be noted that learning, or skill mas-tery, has not truly occurred until the learneracquires the skills, demonstrates the skill withfluency similar to that of a same-age peer,generalizes the skills to new stimuli, and main-tains the skill after instruction ends (Cooperet al., 2007). This study only evaluated theefficiency of that first learning phase, acquisi-tion, and did not account for the other phaseswhich are equally important components oflearning.

In 2015, Smith et al. reported training pro-cedures to teach self-instruction but notedthat they did not teach two critical skills. Theydid not evaluate methods to teach initiationsteps and locating video supports. These twoskills are critical for generalization and use inthe natural environment. The present studyprovides some evidence suggesting that systemof least prompts can be used to teach thesesteps.

Limitations

Readers should interpret the results of thisstudy in the context of several limitations.First, even though SDVM and VSP were stu-dent mediated, the researcher still blockederrors and provided praise on a CRF schedule.Therefore, the students’ skill acquisition didnot occur totally independent of an instruc-tor. Second, mastery criterion was only set atone session at 100% correct, increasing this torequire more demonstrations at 100% correctwould help to evaluate for chance respondingon some steps. Relatedly, the condition notreaching mastery level should have been rununtil the student either mastered with thattask or performance plateaued. This wouldhave provided a more detailed comparison(i.e., total difference in time to mastery) and

would have provided some additional assur-ance that the first task, once mastered, main-tained at that level. Finally, from a designperspective, the multiple opportunity probesappeared to result in a slight acceleratingtrend for all participants in the control condi-tion. This indicates that repeated exposure ofthe steps in sequence will result in at leastsome gradual growth (testing threat; Alexan-der, Ayres, Shepley, Smith, & Ledford, 2017;Alexander, Smith, Mataras, Shepley, & Ayres,2015).

Implications for Research

In terms of research on self-instruction, thisstudy moves closer to providing a methodol-ogy to teach truly independent self-instruc-tion, in that the participants were taught howto initiate their device, select the video corre-sponding to the task direction, and play (andpause during VP) videos independent of in-structor support. Researchers exploring gen-eralization and self-instruction may considerexamining more closely how to eliminate theneed for any instructor engagement. For ex-ample, evaluating means for self-correction/self-evaluation of steps so that a teacher wouldnot be required to interrupt errors. Addition-ally, building in means for self-reinforcementor conditioning task completion alone as areinforcer. To fully manualize a means forself-instruction, more work is required to iden-tify appropriate ways for technology to substi-tute into these traditional teacher roles.

If researchers can design a self-containedsystem that prompts, provides corrective feed-back, and reinforces, which is also accessibleto individuals with disabilities, this wouldmark a step toward a prosthetic technologythat can reshape lives. The ability to pursueone’s own interests and learn what one wantsto learn, would increase autonomy for individ-uals with ID and expand opportunities in lei-sure, work, and independent living.

Implications for Teaching

In a classroom or therapeutic context, prepar-ing self-instructional materials and teachinglearners to control some aspects of their in-struction can free teachers or therapists toreallocate their time to other tasks. In a class-


room, students’ independent work time be-comes an opportunity where they can instructthemselves and learn new skills rather than onlywork on fluency or practicing skills that are al-ready partially in their repertoire. Job coachesand therapists can invest less time in teachingspecific vocational tasks and instead focus onmore nuanced things like social skills.

Teachers can also apply this same method-ology to other multi-step tasks that are part ofthe general education curriculum. For exam-ple, a middle school student learning to solvealgebraic expressions could, in theory, self-instruct on the steps required to solve theproblem by using VSP. This could allow ateacher to structure a session differently ifindividual students could access the learningsupports they needed via technology. Thetechnology exists in the classroom and a greatmany video-based resources exist online. Vet-ting these resources and constructing a systemto provide these means to students after theylearn to self-instruct could help maximizelearning time and engagement in schools.

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High-Probability Request Sequence: An Evidence-BasedPractice for Individuals with Autism Spectrum Disorder

Chelsi R. Brosh, Larry B. Fisher, Charles L. Wood, and David W. TestUniversity of North Carolina Charlotte

Abstract: Wong et al. (2014) conducted a comprehensive review of the literature published between 1990 and2011 to identify practices for children, adolescences, and young adults diagnosed with autism spectrumdisorders (ASD) that had sufficient empirical support to be considered evidence-based. Behavior momentuminterventions (BMI), or high-probability request sequence, was identified as a focused intervention practice withsome support due to an insufficient number of participants. The purpose of this review was to expand on thesefindings from Wong et al. (2014) to determine if high-probability request sequence interventions can beconsidered an evidence-based practice for individuals with ASD. Results indicated the intervention can beconsidered an EBP for individuals with ASD. Implications for practice and suggestions for future researchare discussed.

The Autism and Developmental DisabilitiesMonitoring Network estimates that 1 in 68children are identified as having an autismspectrum disorder (Christensen et al., 2016).According to the American Psychiatric Associa-tion’s Diagnostic and Statistical Manual of MentalDisorders, Fifth Edition (DSM-V), the diagnosticcriteria for autism spectrum disorder (ASD)includes persistent deficits across a range ofseverity in social communication and socialinteraction and restricted, repetitive patternsof behavior, interests, or activities (2013).Due to the complex nature of an ASD diagno-sis, the increased prevalence of the disorder,and the number of unsupported and contro-versial interventions available (Simpson et al.,2005), the need to identify effective and evi-dence-based educational and therapeutic ser-vices for this population has intensified(Odom, Collet-Klingenberg, Rogers, & Hatton,2010).

Federal legislation (ESSA, 2015; IDEA, 2004)requires educators and service providers to useeffective interventions that are based on scien-tifically validated research. The What Works

Clearinghouse (WWC), funded by the Insti-tute for Education Science, has made progressidentifying evidence-based practices (EBP) forgeneral education; however, the identificationof EBP for children and youth with ASD hasbeen slower to emerge due to the omission ofsingle-subject experimental design studies asan acceptable form of empirical evidence(Odom et al., 2010). While there is not auniversally agreed-on set of standards, re-search in special education and autism hasmade progress in identifying practices thatare considered evidence-based (Odom et al.,2010).

The National Standards Project (NSP) ofthe National Autism Center (NAC, 2009) wasone of the first projects to identify evidence-based practices for individuals with ASD. TheNSP developed a model for identifying thelevel of research support that was currentlyavailable on academic and behavioral inter-ventions for students with ASD. The authorsreviewed previously established guidelinesfor examining evidence-based practices fromother related fields (e.g., America Psycholog-ical Association Presidential Task Force onEvidence-Based Practice, 2005; Kratochwill &Stoiber, 2002), examined publications aboutEBPs (e.g., Chambless et al., 1998; Horner etal., 2005), and consulted with 25 experts whoattended planning sessions. After a rigorous

Correspondence concerning this article should beaddressed to Chelsi Brosh, Department of Special Ed-ucation and Child Development, University of NorthCarolina Charlotte, 9201 University City Blvd., Char-lotte, NC 28223. E-mail: [email protected]




review process, the NSP identified a classifica-tion system based on strength of evidence tosupport the targeted practice (i.e., established,emerging, unestablished, ineffective/harm-ful). A total of 775 research studies publishedbetween 1957 and 2007 were reviewed andanalyzed. The NSP identified 11 establishedtreatments (i.e., antecedent packages, behav-ioral packages, comprehensive behavioraltreatment for young children, joint attentionintervention, modeling, naturalistic teachingstrategies, peer training packages, pivotal re-sponse treatments, schedules, self-manage-ment, story-based intervention packages) thatare effective in addressing the core character-istics associated with ASD (NAC, 2009). Inaddition to 11 established treatments, 22 ad-ditional practices were identified as emergingtreatments for individuals with ASD.

In addition to the review conducted by theNSP, in 2010 the National Professional Develop-ment Center (NPDC) on ASD developed a pro-cess for reviewing literature and establishing cri-teria for identifying EBPs for individuals withASD (Odom et al., 2010). Including publishedresearch from 1997 to 2007, the NPDC reviewed775 studies that identified 24 focused interven-tion practices (i.e., prompting, reinforcement,task analysis and chaining, time delay, computer-aided instruction, discrete trial training, nat-uralistic interventions, parent-implementedinterventions, peer-mediated instruction/inter-vention, picture exchange communication sys-tem, pivotal response training, functional behav-ior assessment, stimulus control/environmentalmodification, response interruption/redirec-tion, functional communication training, ex-tinction, differential reinforcement, self-man-agement, social narratives, social skills traininggroups, structured work systems, video model-ing, visual supports, speech-generating devices).The authors determined these 24 practices hadsufficient evidence to be considered EBPs(Odom et al., 2010).

Building on their previous findings (Odomet al., 2010), the NPDC updated and expandedtheir review with more rigorous criteria in2014 (Wong et al., 2014). The authors re-viewed studies published from 1990 to 2011and developed a new evaluation process incor-porating adapted protocols drawing from thequality indicators developed for group design(Gersten et al., 2005), single-case design

(Horner et al., 2005), and the criteria set byWWC and NSP. Studies that were included inthe review (a) targeted individuals with ASDbetween birth and 22 years of age; (b) werebehavioral, developmental, or educational in-terventions implemented in typical educationalintervention settings (i.e., school, home, com-munity); (c) compared interventions to nointervention or alternative intervention condi-tions; (d) had behavioral, developmental, oracademic outcomes; and (e) used an experi-mental group, quasi-experimental group, orsingle-case design (Wong et al., 2014). For afocused intervention to be considered evi-dence-based, the following qualification crite-ria had to be met: (a) at least two high qualityexperimental or quasi-experimental group de-sign studies conducted by at least two differentresearchers or research groups, or (b) at leastfive high quality single-case design studiesconducted by at least three different research-ers or research groups having a total of at least20 participants across studies, or (c) a combi-nation of at least one high quality experimen-tal or quasi-experimental group design articleand at least three high quality single-case de-sign studies conducted by at least two differentresearch groups (Wong et al., 2014). Afteridentifying and reviewing a total of 456 pub-lished studies that met the criteria for inclusion,this review identified 27 EBPs (i.e., antecedentinterventions, cognitive behavioral interven-tions, differential reinforcement, discrete trialtraining, exercise, extinction, functional be-havior assessment, functional communicationtraining, modeling, naturalistic intervention,parent-implemented intervention, peer-medi-ated instruction and intervention, picture ex-change communication system, pivotal responsetraining, prompting, reinforcement, responseinterruption/redirection, scripting, self-man-agement, social narratives, social skills training,structured play group, task analysis, technology-aided instruction and intervention, time delay,video modeling, and visual support). In additionto identifying 27 EBPs, this review found 24other focused intervention practices were iden-tified as having some support, but lacked themethodological criteria used in the review.

One focused intervention practice identi-fied by Wong et al. (2014) as having someempirical support was behavioral momentuminterventions. Wong et al. defined behavior

High-Probability Request Sequence and ASD / 277

momentum interventions as, “the organiza-tion of behavior expectations in a sequence inwhich low probability behaviors are embed-ded in a series of high probability behaviors toincrease the occurrence of the low-p behav-iors” (p. 25). While the field of behavior anal-ysis has used a variety of terms to refer to thisintervention (e.g., interspersed requests,Horner & Day, 1991; pre-task requests, Singer,Singer, & Horner, 1987; behavioral momen-tum, Mace & Belfiore, 1990, high-probabilityrequest sequence, Mace et al., 1988), the termhigh-probability request sequence (HPRS)has become the recommended term for thispractice (Cooper, Heron, & Heward, 2007).HPRS have typically involved the presentationof easy-to follow, or previously mastered re-quests (i.e., on average four) with a high prob-ability of compliance (i.e., high-p) immedi-ately before presenting a request that hasbeen identified as having a low probability(low-p) of compliance (Lee, 2005; Sprague &Horner, 1990). HPRS provides a non-aversiveantecedent-based procedure that has empiri-cal support as being an effective interventionfor increasing compliance across a variety ofsettings and behaviors (Lee, 2005).

While Wong et al. (2014) found behavioralmomentum interventions have support from asufficient number of single-case design studies(Banda & Kubina, 2006; Davis, Brady, Hamil-ton, Evoy & Williams, 1994; Davis, Brady, Wil-liams, & Hamilton, 1992; Ducharme, Lucas, &Pontes, 1994; Houlihan, Jacobson, & Bran-don, 1994; Jung, Sainato, & Davis, 2008; Patelet al., 2007; Riviere, Becquet, Peltret, Facon, &Darcheville, 2011; Romano & Roll, 2000);however, this collection of studies did not in-clude a sufficient number of participants (i.e.,20 or more) across studies to be consideredevidence-based. Furthermore, their review wasmore broadly applied to research on strategiesbased on the concept of behavioral momen-tum rather than the specific HPRS strategy.Therefore, the purpose of this comprehensiveliterature review was to expand the findings ofWong et. al. (2014) to include the literatureon HPRS published from 2012–2016 using theevidence-based criteria developed by Wong et.al. (2014) and to determine if HPRS can beconsidered an EBP for individuals with ASD.

Method

A comprehensive review of the literature pub-lished from 2012 to 2016 was conducted toextend the review by Wong et al. (2014) toevaluate the effects of HPRS on the behav-ioral, developmental, or academic outcomesfor individuals with ASD between birth and 22years of age. This extended review involved adirect replication of Wong and colleagues’(2014) procedures for article identificationand selection, inclusion/exclusion criteria,and review process.

Inclusion Criteria

Participants and outcomes. To qualify for thereview, a study had to include participantsbetween birth and 22 years of age and diag-nosed with ASD. Participants who were iden-tified as having comorbid diagnoses (e.g., au-tism and intellectual disability, Davis et al.,1992) were included in the review. HPRS in-terventions studies reporting behavioral, de-velopmental, or academic outcomes were in-cluded in this review.

HPRS interventions. The HPRS interven-tion was defined as the presentation of threeor four high probability requests prior to thepresentation of a low probability request (Maceet al., 1988).

Research design. Studies utilizing an exper-imental group, quasi-experimental, or single-case design (SCD) to investigate the efficacy ofHPRS were included in this review. SCD stud-ies accepted for this review were withdrawal oftreatment, multiple baseline, multiple probe,alternating treatment, and changing criteriondesigns (Kratochwill et al., 2013; Wong et al.,2014).

Search Procedures

Screening of articles identified by Wong et al.(2014). Nine articles identified as havingmet criteria for qualification as an EBP in theWong et al. (2014) review were screened todetermine inclusion. Article titles and ab-stracts were screened to determine if the pro-cedures used in the behavioral momentuminterventions utilized a HPRS based on thedefinition provided by Mace et al. (1988).One article was identified as not utilizing a


HPRS (Ducharme et al., 1994) and was ex-cluded from this review, leaving a total ofeight articles from Wong et al.

Expanded literature search. A literature searchwas conducted using electronic print sourcesusing a university’s electronic library database(e.g., ERIC, Education Research Complete,Academic Search Complete, ArticleFirst, World-Cat.org, ScienceDirect, PsycARTICLES, Psy-cINFO, Social Work Abstracts) and GoogleScholar. To expand and evaluate HPRS as anEBP, a search of the literature using key wordsassociated with a diagnosis of autism (i.e., au-tism, autism spectrum disorder, ASD) andHPRS (i.e., high-probability request sequence,high-p request sequence, high-p low-p re-quests, behavior momentum interventions,behavioral momentum, interspersed requests,pre-task requests, high probability instruc-tional sequence, high probability sequence)was conducted to include studies publishedin peer reviewed journals between 2012 and2016. Twenty-three articles were identifiedas being potentially eligible for inclusion inthis review. Article titles and abstracts werescreened to determine (a) experimentalmethodology, (b) target population, and (c)use of HPRS. After screening, 14 articles wereexcluded based on title and three articles wereexcluded based on abstract review, resultingin six articles published between 2012 and2016 that were included in the final review.Combined with the eight articles identified byWong et al. (2014), 14 total articles were in-cluded in this review. Agreement for inclusionand exclusion of articles was 100%.

Review of Literature

Coding for quality indicators. Two doctoralstudents in special education served as review-ers. Reviewers used the single-case design EBPinclusion criteria checklist developed by Wonget al. (2014) based on the work of Horner etal. (2005), Nathan and Gorman (2007), Rog-ers and Vismara (2008), and the earlier workby APA Division 12 (Chambless & Hollon,1998) to code articles. The EBP criteria check-list included information about (a) the depen-dent variable, (b) research questions, (c) mea-surement system, (d) interoberser agreement(IOA), (e) clear description of proceduresutilized in baseline and intervention, (f) re-

peated measures shown in a graphical format,and (g) at least three different replicationsacross time or phases. If a study met all qualityindicators listed on the EBP checklist, thestudy was included as evidence supportingHPRS as an EBP.

Interrater reliability on quality indicators. In-ter-rater reliability was calculated using anitem-by-item analysis of the quality indicatorchecklist. Inter-rater agreement was collectedfor 100% of the articles across reviewers. Theformula for inter-rater agreement was totalagreements divided by agreements plus dis-agreements multiplied by 100. Two levels ofagreement were calculated to include agree-ment on individual items of the criteria check-list and the agreement on the summative eval-uation of whether the study met or did notmeet criteria for inclusion. Mean inter-rateragreement for both levels was 100%.

Results

Fourteen studies published in peer-reviewedjournals between 1992 and 2016 were iden-tified and coded using a quality indicatorchecklist. Table 1 summarizes descriptive in-formation about each study included in thereview.

Descriptive Analysis of Literature Reviewed

Table 1 provides a summary of the descriptioninformation for the 14 studies included in thisreview. Twenty-six participants diagnosed withASD were included in the studies reviewed.Participants ranged from 3 to 15 years old.The 14 studies reviewed used SCD. Specifi-cally, five studies used a multiple baseline de-sign (Davis et al., 1992, 1994; Esch & Fryling,2013; Jung et al., 2008; Meier, Fryling, & Wal-lace, 2012), five studies used a withdrawal oftreatment design (Banda & Kubina, 2006;Ewry & Fryling, 2016; Patel et al., 2007; Pitts &Dymond, 2012; Romano & Roll, 2000), threestudies used an alternating treatments design(Houlihan et al., 1994; Penrod, Gardella, &Fernand, 2012; Riviere et al., 2011), and onestudy used a multiple probe design (Kelly &Holloway, 2015). Thirteen studies reviewed re-ported behavioral (i.e., compliance, social in-teractions, food acceptance) outcomes. One


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study reported educational (tact fluency) out-comes.

Quality of Literature

A total of 14 studies met the inclusion criteriafor HPRS as an EBP for individuals with ASD.After coding for quality indicators using theinclusion criteria checklist, all 14 studies(100%) were identified as having met thecriteria to support HPRS as an EBP. Table 2highlights the eight studies identified byWong et al. (2014) included in the review andthe EBP inclusion criteria checklist. Table 3indicates the additional six studies identifiedin the expanded search to include literaturepublished in peer review journals between2012 and 2016 and the EBP inclusion criteriachecklist. Of the literature reviewed, eachstudy had (a) a dependent variable alignedwith the research question or purpose of thestudy, (b) a clearly defined dependent vari-able, (c) a measurement system aligned withthe dependent variable that produced a quan-tifiable index, (d) a second observer collectdata on the dependent variable for at least20% of sessions across conditions, (e) interob-server agreement of 80% or higher, (f) a clearlydescribed independent variable, (f) a clearly de-scribed baseline, (g) results displayed in a graph-ical format showing repeated measures acrosstime, and (h) results demonstrating change inthe dependent variable when the independentvariable is manipulated by the experimenter atthree different points in time or across threephase repetitions.

HPRS as an EBP

To classify HPRS interventions as an EBP forindividuals with ASD, at least five quality SCDstudies conducted by three different researchgroups and involving a total of 20 or moreparticipants across studies must be supported(Wong et al., 2014). After coding for qualityindicators and analyzing data, 14 studies inves-tigating HPRS for individuals with ASD wereconducted by 12 different research groups.The 14 studies included 26 participants diag-nosed with ASD. The findings of this literaturereview suggest that HPRS can be considered asan EBP for individuals diagnosed with ASD.

Discussion

The purpose of this comprehensive literaturereview was to expand the findings of Wong et.al. (2014) to evaluate the literature on HPRSpublished from 2012–2016 using the evidence-based criteria developed by Wong et. al.(2014) and to determine if HPRS can be con-sidered an EBP for individuals with ASD. Re-sults of the expanded EBP review add to thefindings of Wong et al. (2014) by identifyingHPRS as an EBP for individuals with ASD.Based on literature published in peer-re-viewed journals between 2012–2016, six addi-tional studies were found to add to existingliterature to the Wong et al. findings support-ing the effectiveness of HPRS interventionsfor individuals with ASD. Present resultsindicated an additional 11 participants wereincluded from a total of six, high-quality sin-gle-case design studies conducted by four dif-ferent groups. When combined with the workof Wong et al. (2014), HPRS interventionsnow have a total of 26 participants, across 14high-quality single-case design studies con-ducted by 12 different investigators or re-search groups. Based on the criteria set byWong et al. (i.e., at least five high qualitysingle case design studies, conducted by atleast three different researchers or researchergroups, and having a total of at least 20 par-ticipants across studies), HPRS can be consid-ered an EBP for students with ASD. Together,these findings indicate HPRS are effectivewhen implemented with students with ASDacross academic, behavioral, and developmen-tal domains (e.g., acceptance of low-p foods,fluency of low-p tacts, low-p compliance, aca-demic, non-academic), settings (e.g., class-room, home, doctor’s office), and participants(e.g., ages 3 to 20).

Limitations

As evident in the literature review process, adiverse use of terminology (e.g., behavioralmomentum interventions, high-probability in-structional sequence, high-probability requestsequence, interspersed requests) used to rep-resent the focused intervention practice (i.e.,HPRS) complicated the process of identifyingand coding peer-reviewed studies. It is possi-ble that other studies could have been in-


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cluded in the review, but variation in termi-nology made it difficult to identify relevantstudies. Furthermore, studies included in thereview varied in procedural detail such as (a)the time between high-p and low-p requests,(b) the frequency and time of reinforcementgiven after complying with low-p requests, and(c) the use of peer modeling. These addi-tional variables should be taken into consid-eration when evaluating the level of evidencethat supports this practice.

Suggestions for Future Research

Additional research should investigate the ef-fectiveness of HPRS used in treatment pack-ages (e.g., HPRS with low-p demand fading,Penrod et al., 2012; HPRS with and withoutprogrammed reinforcement, Wilder, Majda-lany, Sturkie, & Smeltz, 2015). Further, it isimportant to investigate the effects of this fo-cused treatment practice across participantages, skill levels, and activities (e.g., academicinstruction). In addition, future researchshould evaluate the intervention’s effects onparticipants’ maintenance and generalizationof target behaviors. The majority of the arti-cles in this review demonstrated that adults(i.e., parents, teachers, therapists) in varioussettings could implement a HPRS with fidelity.Few studies (i.e., Davis et al., 1994; Jung et al.,2008) attempted to use peers as the primaryinterventionist. Future research should inves-tigate the use of peers without disabilities toincrease social interactions. In addition to theuse of various adults, studies included in thisreview also varied on what met criteria to beconsidered high-probability behaviors. Onlyone study was found that investigated usingmedium-probability behaviors in their inter-vention package (i.e., Romano & Roll, 2000).Romano and Roll suggested that a larger poolof requests could be used by broadening thedefinition of the types of requests (i.e., high-pand medium-p) that could be implementedprior to low-p requests. Future research shouldcontinue to investigate medium-probability re-quests as a means to increase compliance tolow-probability behaviors.

Implications for Practice

When HPRS is used as an antecedent-basedintervention, it can be easy to implement

(Meier et al., 2012), used to increase skillacquisition, and prevent problem behavior.With an increased emphasis on the use of EBPacross educational and therapeutic settings,additional training and support should begiven to educators and practitioners to in-crease awareness of this strategy. As an EBPfor individuals diagnosed with ASD, HPRScould be used to increase skill acquisition andcompliance across various settings. This inter-vention package requires limited training, nomaterials, and is cost effective.

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Word Study Intervention for Students with ASD: A MultipleBaseline Study of Data-Based Individualization

Michael SolısUniversity of California Riverside

Farah El ZeinBrescia University College

Marie Black, Alexandra Miller, William J. Therrien, and Marcia InvernizziUniversity of Virginia

Abstract: This multiple baseline across participants study examined the efficacy of a data-based individual-ization word study intervention for students with autism spectrum disorder (N � 5) and low word readingskills. An experienced interventionist provided 1:1 word reading instruction in 30-minute sessions five times perweek for an average of 10 sessions per participant. Intervention effects for directly taught words and words withsimilar spelling patterns were estimated using visual analysis and calculation of mean differences acrossbaseline and intervention phases. Results indicate immediate and consistent improvements in word readingoutcomes across all participants.

Recent reports suggest that as many as one inthree students with autism spectrum disorder(ASD) have reader profiles of low decodingskills and comprehension (Nation, Clarke,Wright, & Williams, 2006; Wei, Christiano, Yu,Wagner & Spiker, 2014). Nation et al. (2006)reported 42% of students with ASD in theirsample as having low decoding skills in addi-tion to low reading comprehension. More re-cently, Wei et al. (2014) reported a similarfinding of 32% of students with ASD havinglow decoding. Despite this being the case,reading interventions for students with ASDover the last 10 years have primarily focusedon reading comprehension (Chiang & Lin,2007; El Zein, Solis, Vaughn, & McCulley,2014; Whalon, Al Otaiba, & Delano, 2009).The overwhelming focus on comprehension

instruction is not surprising considering theinfluential reader profile studies conductedover several decades which described averageor above average decoding skills with low com-prehension for students with ASD (Frith &Snowling, 1983; Goldberg, 1987; Minshew,Goldstein, Taylor, & Siegel, 1994; O’Connor& Hermelin, 1994; Pattie & Lupinetti, 1993).While there does appear to be agreementamong many researchers on the pervasive dif-ficulties students with ASD experience withreading comprehension (Fluery et al., 2014),it is also important to note the needs of stu-dents with ASD that do not fit this typicalprofile. Beyond comprehension needs, it isalso important to investigate other challengesfaced by students with ASD with less typicalreader profiles such as those who face chal-lenges with decoding and word reading.

Code-Based Reading Intervention Research forStudents with Autism Spectrum Disorders

Whalon and colleagues (2009) conducted asynthesis of reading interventions for studentswith ASD that identified eleven studies provid-ing instruction in decoding, fluency, vocabu-lary, and comprehension. Of these 11 studies,only three pre/posttest design and one single-case design study investigated code-based in-

The authors would like to thank Hannah Mathewsfor her hard work on materials development and datacollection. This research was supported by the Instituteof Education Sciences, U.S. Department of Education,through Grant R324A160299 to the University of Cal-ifornia Riverside. The opinions expressed are those ofthe authors and do not represent the views of theInstitute or the U.S. Department of Education. Corre-spondence concerning this article should be ad-dressed to Michael Solis, Graduate School of Edu-cation, University of California Riverside, Riverside,CA 92521. E-mail: [email protected]


Word Study Intervention and ASD / 287


terventions (Basil & Reyes, 2003; Coleman-Martin, Heller, Chihak, & Irvine, 2005;Heimann, Nelson, Tjus, & Gilberg, 1995; Tjus,Heimann, & Nelson, 1998). Three of the stud-ies used modified versions of the same com-puter assisted instructional software program(Basil & Reyes, 2003; Heimann et al., 1995;Tjus et al., 1998). The software program pro-vides opportunities for students to selectwords and word groups to form sentences withtasks over time requiring increasingly morecomplex grammatical structures. Across thesestudies, the findings showed some promise yetwere inconclusive due to a number of meth-odological issues including small sample sizeswith large age ranges, unclear screening proce-dures, and lack of comparison or control condi-tions.

Basil and Reyes (2003) investigated the soft-ware program along with a scaffolding ap-proach in a pre/posttest design. Two studentswith ASD, ages 8 and 14, received 12 hours ofinstruction in 30 min. sessions twice per week.While both students “mastered” the lessonsaccording to the software-based proximalmeasure, only one student made significantgains from pre to post-test on measures ofphonological awareness (PA) and word spell-ing. Heimann et al. (1995) investigated thesoftware program with a larger sample size ofstudents with ASD (N � 11) ranging in agefrom 6 to 14 years old. Significant gains inreading was reported, however, no significantgains were detected on PA measures. Tjus etal. (1998) also reported mixed results for stu-dents with ASD with significant gains on PAand reading measures reported on initial post-tests but no significance differences found ondelayed post-test measures.

Utilizing a multiple condition design withdrop-down baselines, Coleman-Martin et al.(2005) investigated a computer-assisted in-structional (CAI) approach using a nonverbalreading approach for word identification forone student with a dual diagnosis of ASD andmoderate intellectual disability. Researcherscompared baseline phases to teacher only,teacher plus CAI, and CAI only. The student,‘Carrie’, had low mean scores on percent ofwords identified correctly (M � 13.3%) dur-ing baseline. The remaining baseline phasesstayed consistently low (M � 0.0%). Acrossthe three treatment conditions the teacher

only phase had the steepest slope with a clearupward trend ending with Carrie scoring 80%correct on three of her last four scores. Ini-tially during the teacher plus CAI conditionthe scores dropped to 60% correct. However,at the end of this phase Carrie scored 100%correct on her last two scores. During the CAIonly phase, scores ranged from 60% to 80%correct with the exception of a few outliers.

Following the synthesis by Whalon et al.(2009) some additional studies were con-ducted that investigated word study interven-tions for students with ASD. Infantino andHempenstall (2006) investigated a direct in-struction intervention of word reading thatused standardized protocols for the basis ofinstruction (Infantino & Hempenstall, 2006).After 23 hours of instruction, one student withASD did not show significant gains in decod-ing skills with no differences in percentilerank, age and grade equivalents. In a multiplebaseline across probes design, Whitcomb,Bass, and Luiselli (2011) reported improvedaccuracy with word lists after one student withASD participated in five intervention sessionsprovided through computer assisted instruc-tion. Similarly, in a multiple baseline acrossprobes study, Yaw et al. (2011) reported animmediate increase in sight word readingacross 16 intervention sessions delivered toone grade 6 student through computer as-sisted instruction.

Rationale and Hypotheses

Initial data on reading performance for thisstudy were taken from a larger observationstudy investigating issues of reading instruc-tion for students with ASD (Solis, Black, Ro-mig, & Miller, in development). The districtpersonnel informed us of their concern withstudents’ word reading skills rather than read-ing comprehension, which were further con-firmed from our testing results. We thenworked with district personnel to design astudy that would integrate in with their cur-rent efforts to address the problem ratherthan supplant any attempts at intervention.Utilizing the techniques described by theNational Center on Intensive Interventions(2013), we adapted the word study readingprogram that was in use by the school district,Words Their Way (Bear, Invernizzi, Templeton,


& Johnston, 2016), by infusing explicit in-struction and corrective feedback through adata-based individualization (DBI) approachto intervention. The Words Their Way DBI(WW-DBI) approach was designed to addressthe most intense instructional needs for stu-dents who have not responded to previousinterventions (National Center on IntensiveInterventions, 2013).

The purpose of this study was to determineif adaptations of the district adopted wordstudy curricula would increase performanceword study skills for words directly taught andfor words that followed the same spelling pat-terns. The study addressed the following re-search hypotheses: The WW-DBI interventionwill result in improved performance on wordsdirectly compared to baseline performance.The WW-DBI intervention will result in im-proved performance on words not taught butthat followed the same word patterns as thosetaught compared to baseline performance.

Method

Setting and Instructor

All participants were from one rural schoollocated in a southeastern state. According todata from the State Department of Education,the racial and ethnic population of studentsin the district at the time of the study includedthe following: Caucasian, 73.9%; African Amer-ican, 13.0%; Hispanic, 6.90%; two or moreraces, 5.0%; Asian, 0.50%; Native American,0.30%. All intervention sessions were conductedin a small room adjacent to the school library.No other students were present during the in-tervention. Sessions were held during students’intervention or resource period and scheduledfor 30 minutes per day, five days per week.

The instructor was a retired special educa-tor who previously worked at the school dis-trict for 30 years. She had extensive back-ground in teaching reading to children withdisabilities. She was hired, trained, and super-vised by the research team.

Participants

Students. Four male students and one fe-male student with ASD (as identified by theschool district through the multi-disciplinary

team process) in grades 5–7 participated inthe study (N � 5). All students were Cauca-sian. According to school district personnel,students all received a combination of generaleducation and special education classes withminimal behavior supports provided as needed.Parent consent and student assent were ac-quired for all participants as approved by theInstitutional Review Board from the universityof the first author. As part of the screeningprocedure, participants were administered twostandardized reading measures: the Test ofSentence Reading Comprehension (TOSREC)(Wagner, Torgesen, Rashotte, & Pearson, 2010)and the Test of Word Reading Efficiency(TOWRE) (Torgesen, Wagner, & Rashotte,1999). The Kaufmann Brief Intelligence TestVerbal (KBIT-2) (Kaufman & Kaufman, 2004)was also administered to provide descriptivedata. To qualify for the study, students neededto meet the following criteria: TOSREC stan-dard score � 85, TOWRE standard score � 80.To determine the appropriate starting point forintervention, each students was administeredthe spelling inventory placement test fromWords Their Way to identify their current wordpart knowledge. See Table 1 for a summaryof participant information including ageand grade placement.

Materials

Individualized pool of unknown words. Weadapted the procedures described by Ferkis,Belfiore, and Skinner (1997) to guide the de-velopment of the word lists. Using words se-lected from Words Their Way (Bear et al.,2016), we developed a pretest probe to deter-mine unknown words for each participant.Prior to administration of the pretest, thewords were leveled for frequency using theCorpus of Contemporary American English(COCA; http://corpus.byu.edu/coca/) withhigh frequency words being removed. Theseunknown words, in turn, were used to developthe word banks included in the baselineprobes, intervention materials, and interven-tion probes of taught words. The initial wordprobe was delivered on an iPad using stan-dard-sized PowerPoint slides. Each slide con-tained 10 words from a single developmentallevel of the Words Their Way scope andsequence (Bear et al., 2016). Each student’s


http://corpus.byu.edu/coca/

level was determined by the Words Their Wayspelling inventory placement test (Bear et al.,2016) (see Table 1), and each student’s scopeand sequence was individualized accordingly.

Scope and sequence development. In order todevelop each student’s individualized scopeand sequence, we compiled and sorted allunknown words by spelling feature. Using theprinciples of word study and minimally dis-criminant pairs, we developed weekly wordsorts that followed the developmental spellingsequence established in Words Their Way (Bearet al., 2016). Each sort contained 15 words, 10words established through the pre-assessmentas unknown to the student, and a set of fivegeneralization words following the same spell-ing pattern. This scope and sequence was usedto develop the lessons, baseline and interven-tion probes, and curriculum-based measures.

Measures

Screening and descriptive measures. Threestandardized measures were administered tostudents prior to baseline data collection. TheTest of Sentence Reading Comprehension(TOSREC), Test of Word Reading Efficiency–Second Edition (TOWRE–2), and the Kauf-man Brief Intelligence Test – Second Edition(KBIT-2).

Test of Silent Reading Efficiency and Compre-hension (TOSREC; Wagner et al., 2010). TheTOSREC is a 3 min, group-administered as-sessment of reading fluency and comprehen-sion. Students are presented with a series ofshort sentences and asked to read silently andassess whether the sentences are true or false.

Average alternate-form coefficients range from0.84 to 0.95.

The Test of Word Reading Efficiency (TOWRE:Torgesen et al. 1999). The TOWRE consists oftwo individually administered 45-second sub-tests of sight word reading and phonemic de-coding efficiency. Each list of words and non-words starts with the least-difficult items andgradually increases in difficulty. The alternate-forms reliability coefficients were reported as0.91 to 0.97 (Torgesen et al., 1999).

Kaufman Brief Intelligence Test–Second Edition(KBIT-2; Kaufman & Kaufman, 2004). TheKBIT-2 is individually administered in approx-imately 15 min; it assesses both verbal andnonverbal ability in people from 4 through 90years of age. The KBIT-2 is composed of twoseparate scales. The Verbal Scale contains twokinds of items---Verbal Knowledge and Rid-dles---both of which assess crystallized ability(knowledge of words and their meanings).The items cover both receptive and expressivevocabulary, and they do not require readingor spelling. Composite internal consistency re-liabilities were ranged from 0.89 to 0.96. Va-lidity studies yielded moderate to high corre-lations with both construct and concurrentvalidity studies (Kaufman & Kaufman, 2004).

Dependent measures. Based on the individu-alized pool of unknown words, daily 20-wordprobes were administered during the baselinephase. During the intervention phase, eachprobe contained 10 words that were directlytaught as a part of the word sort, five wordsthat were not included in the word sort butfollowed the same spelling patterns as those inthe sorting activity, and five distractor words

TABLE 1

Participant Information

Participant Age (years) Grade KBIT Verbal* TOSREC* TOWRE* WWSI

Edward 11 6 79 68 77 Late syllable with affixesTony 13 7 78 76 76 Early within wordRichard 13 6 87 85 63 Late within wordJulia 12 6 66 61 54 Middle letter nameJustin 10 5 80 64 77 Early within word

Note: * � Reported as standard scores; KBIT � Kaufman Brief Intelligence Test; TOSREC � Test of SentenceReading Efficiency and Comprehension; TOWRE � Test of Word Reading Efficiency; WWSI � Words TheirWay Spelling Inventory placement information.


used to control for cueing during the probes.The daily probes contained two dependentmeasures. The first measure was students’identification of the 10 explicitly taughtwords. These words served as a measureof students’ acquisition of directly taughtwords. The second measure consisted of fivewords not directly taught. These wordsserved as a measure of students’ ability togeneralize taught spelling patterns to novel,unknown words.

Probes were delivered using a single wordlist each day. Directly taught words remainedthe same for the 5-day instructional cycle, gen-eralization words varied across days of theweek. When recording word reading accuracy,the tutor allowed 5 seconds per word for a stu-dent response and allowed for unprompted,self-corrections within those 5 seconds. Onepoint was awarded for each correctly pro-nounced word. See Figure 1 for an annotatedexample of a daily probe.

Procedure

Tutor training. We trained one tutor, a re-tired special educator with up-to-date teachercertification, in two 2-hour sessions. The firstday of training consisted of reviewing the prin-ciples of word study (Bear et al., 2016), theprinciples of explicit instruction (Archer &Hughes, 2011), and error correction proce-dures established by the first author of thestudy. The second day of training consisted of

detailing the specific instructional routineand materials and having the tutor conductmock sessions of the intervention until shedemonstrated mastery of the instructional se-quence. Throughout the training, the tutorwas encouraged to balance following the in-structional routines along with maintainingsome instructional flexibility which is a corefeature built into Words Their Way (Bear et al.,2016).

Intervention. The word study interventionWW-DBI consisted of daily teacher-directedword sorts and guided practice in word fea-ture analysis. The intervention was an adaptedversion of the word study instructional routinedetailed by Bear et al. (2016). To better alignto the needs of students with ASD, we madethe following modifications: instruction wasprovided one to one instead of in groups,increased teacher modeling and guided feed-back (Archer & Hughes, 2011) during explo-ration, additional instructor prompts and scaf-folds during guided practice, and increasedintervention duration. The tutor used the cur-rent behavior management system of positivereinforcement as outlined on each student’sindividualized education plan.

Students met individually with the tutor forfive days per week for 15- to 20-minutes ofinstruction (30 min with probe administra-tion). Words used in each sort were 50%known and/or high frequency words and 50%unknown to the student. During instruction,the following 7-step process was followed.

Figure 1. Teacher copy of the daily word probe.


(1) The instructor delivered a goal statementfor the lesson, and after session one, a reviewwas provided on the previous day’s word sort.(2) The tutor reviewed the new sort by estab-lishing word categories and reviewing thewords’ pronunciations and meanings. (3) Thetutor demonstrated how to sort the words bysound and repeated the process for sorting bysight or spelling patterns. (4) Using a teacher-directed, closed sort, the tutor guided the stu-dent through the process of sorting the wordsunder the given headers, working through thewords one at a time. Using the sentenceframes provided as a meta-cognitive guide,the instructor introduced each word andprompted discussion about the words regard-ing position of spelling features within theword, frequency, and related words. (5) Thetutor gradually released this responsibility tothe students, providing praise and correctivefeedback. (6) The students led the sort (inde-pendent practice) while the tutor promptedthe student to say the words and comparethem as they sorted. (7) Following completionof the sort, the tutor guided the studentsthrough a reflection process. During the re-flection process, the tutor prompted the stu-

dent to compare and contrast the words andtheir features. Sentence frames such as “I no-tice that . . .” and “When I read these words, Ihear . . .” were used to promote students’ useof feature analysis. To encourage students tothink about their rationale for sorting wordsand making connections to the words’ sounds,spellings, and meanings, the tutor asked thestudent to explain why they sorted the wordsas they had. This explanation of students’thinking was recorded on a flip chart to beused during the following day’s lesson.

Procedural Integrity and Reliability

All intervention and assessment sessions wereaudio-recorded. We used these recordings tocheck a random sample of 20% of the inter-vention sessions for procedural integrity andreliability. From the audio recordings, a re-searcher familiar with the intervention usedan implementation validity checklist of thecore instructional steps of the intervention todetermine the percent of expected instruc-tional steps. See Figure 2 for an example oftreatment integrity protocol. The integrity

Figure 2. Fidelity checklist for day one of intervention instruction.


and reliability of implementation was 82% forthe coded sessions.

Reliability of assessment data was assessedfor 28% of the sessions by rescoring the sessions’dependent measures and comparing the scoresobtained to the scores reported by the tutor. Atotal of 35 sessions were dual coded by tworaters. Interobserver agreement was then deter-mined by calculating the total number of agree-ments divided by the total number of agree-ments and disagreements multiplied by 100.The mean agreement across observers was96.1%.

Experimental Design and Data Analysis

A single-case multiple baseline design acrossparticipants was used to evaluate the effects ofthe intervention on participants’ abilities toread words in isolation. The advantage of amultiple baseline design, unlike a reversal de-sign, is it allows for the empirical examinationof dependent measures (i.e., word reading)that do not reverse upon removal of the inter-vention (Tawney & Gast, 1984). Furthermore,the sequential implementation of the inde-pendent variable parallels the practices ofteachers and generalization of the behaviorchange is monitored through the design (Gast,Lloyd, & Ledford, 2014).

Researchers have traditionally used the vi-sual analysis method to interpret single casestudy results (Kratochwill et al., 2010). Cur-rently, there is not consensus regarding onestatistical analysis procedure to interpret re-sults from single-case design studies (Kratoch-will et al., 2010). For these reasons, we ana-lyzed data based on visual inspection of thegraph for each participant. Visual inspectionof the graphs was based on the (a) level, (b)trend, (c) variability, (d) overlap, (e) immedi-acy of the effect, and (f) consistency of datapatterns across similar phases (Kratochwillet al., 2010).

Results

Edward

Edward’s word reading data are presented inFigure 3. During baseline, Edward’s scores ondaily reading probes ranged from 20% to 60%with a mean of 45%. Upon implementation of

WW-DBI intervention, Edward’s level of taughtand untaught word reading accuracy increasedto means of 86% and 68% respectively. Scoresfor taught words ranged from 60% to 100%,and scores for untaught words ranged from0% to 100% (See Table 2). Upon introduc-tion of the intervention, an immediate increasein Edward’s score is observed for taught wordsand a gradual increase is observed for untaughtwords. Visual inspection of the graph reveals anupward trend for both taught and untaughtwords during the intervention phase. Calcula-tion of overlapping data between interventionand baseline phases shows 5% overlap fortaught words and 58% overlap for untaughtwords.

Tony

Tony’s word reading data are presented inFigure 3. Tony’s scores on daily readingprobes ranged from 20% to 50% during base-line phase, with a mean of 38%. Upon imple-mentation of WW-DBI intervention, Tony’slevel of taught and untaught word readingaccuracy increased to means of 98% and 71%respectively. Scores for taught words rangedfrom 90% to 100%, and scores for untaughtwords ranged from 40% to 100% (See Table2). Upon introduction of the intervention, animmediate increase in Tony’s score is ob-served for both taught and untaught words.Visual inspection of the graph reveals an up-ward trend for both taught and untaughtwords during the intervention phase. Calcula-tion of overlapping data between interventionand baseline phases shows 0% overlap fortaught words and 8% overlap for untaughtwords.

Richard

Richard’s word reading data are presented inFigure 3. During baseline, Richard’s scores ondaily reading probes ranged from 15% to 55%with a mean of 29%. Upon implementationof WW-DBI intervention, Richard’s level oftaught and untaught word reading accuracyincreased to means of 95% and 74% respec-tively. Scores for taught words ranged from70% to 100%, and scores for untaught wordsranged from 40% to 100% (See Table 2).Similar to Edward’s results, upon introduction


of the intervention phase, an immediate in-crease in Richard’s score is observed fortaught words and a gradual increase for un-

taught words. Visual inspection of the graphreveals an upward trend for both taught anduntaught words during the intervention

Figure 3. Word reading accuracy reported as percentage of words read correctly.


phase. Calculation of overlapping data be-tween intervention and baseline phases shows0% overlap for taught words and 10% overlapfor untaught words.

Julia

Julia’s word reading data are presented inFigure 3. During baseline, Julia’s scores ondaily reading probes ranged from 5% to 60%with a mean of 31%. Upon implementation ofWW-DBI intervention, Julia’s level of taughtword reading accuracy increased to a mean of70%, and her untaught word reading accuracyincreased to a mean of 37%. Scores for taughtwords ranged from 40% to 90%, and scoresfor untaught words ranged from 20% to 60%.Upon introduction of the intervention, agradual increase then drop in Julia’s score isobserved for both taught and untaught words.Visual inspection of the graph reveals a neu-tral trend for both taught and untaught wordsduring the intervention phase. Calculation ofoverlapping data between intervention andbaseline phases shows 43% overlap for taughtwords and 100% overlap for untaught words.

Justin

Justin’s word reading data are presented inFigure 3. During baseline, Justin’s scores ondaily reading probes ranged from 25% to 80%with mean of 58%. Upon implementation ofWW-DBI intervention, Justin’s level of taughtand untaught word reading accuracy in-creased to means of 87% and 93% respec-tively. Scores for taught and untaught words

ranged from 80% to 100%. Upon introduc-tion of the intervention, a gradual increase inJustin’s score is observed for taught and un-taught words. Visual inspection of the graphreveals an upward trend for untaught wordsand a neutral trend for taught words. Calcu-lation of overlapping data between interven-tion and baseline phases shows 67% overlapfor taught words and 33% overlap for un-taught words.

Discussion

The purpose of this study was to examine theimpact of an adapted version of a widely usedword study program on the word reading skillsof students with ASD. Our aim was to examinethe effectiveness of the intervention of wordsdirectly taught and words not directly taughtthat followed the same spelling patterns whenimplemented by a tutor hired, trained, andsupervised by researchers. We hypothesizedthat with adaptations to the program, this ap-proach is likely to improve performance forstudents with a history of very low perfor-mance with word reading (TOWRE, standardscore M � 69.4).

Effectiveness of Intervention

Results indicate that the WW-DBI interventionwas generally effective for improving perfor-mance with words directly taught and moder-ately effective with untaught words for stu-dents with ASD with low word reading skills.These gains in word reading skills are similarto findings reported in previous work (Cole-man-Martin et al., 2005; Infantino & Hempen-stall, 2006; Whitcomb et al., 2011; Yaw et al.,2011). During the baseline condition, stu-dents on average read 45.8% words correctly.During the intervention condition, studentsread an average of 87.2% words directlytaught correct and an average of 68.6% ofwords not directly taught. All five participantshad mean scores of words taught well abovetheir baseline performance, and four of thefive participants had mean scores of words notdirectly taught above their baseline perfor-mance.

There was a clear immediacy effect frombaseline to intervention across all five partici-pants. The average baseline score just prior to

TABLE 2

Word Reading Accuracy on Daily Reading Probes

ParticipantBaseline

(%)Taught

(%)Not

Taught (%)

Edward 45 86 68Tony 38 98 71Richard 29 95 74Julia 31 70 37Justin 58 87 93

Note: Scores represent percentage of words readcorrectly during the daily reading probes.


beginning intervention was 36% for wordstaught with the average score of words taughtwhen starting intervention was 78%. The dataindicate clear intervention effect at five pointsin time across baseline and intervention witheach phase having three or more data points.In fact, the data “meets standards” as outlinedby Kratochwill et al. (2010) for demonstratingan effect by having four phases with at leastfive data points per phase. Across four of thefive participants the variability was relativelylow for words directly taught with more vari-ability with words not directly taught. It shouldalso be noted that findings from the screeningmeasures indicated much lower performancefor the one participant that did not performsimilarly to the others. Across both dependentmeasures, there was a neutral trend duringthe intervention phase of higher performancecompared to baseline for four of the five par-ticipants. These findings suggest that whendifficult words are targeted and explicitlytaught to students, their performance in com-pleting the task almost doubles. Even morecompelling is evidence that the particular skillbeing instructed did in general transfer towords not directly taught. In other words, theadaptations to the word study program usedby the school district appear to have improvedperformance for all participants.

We expected the procedural integrity to beabove 90% during the sessions. The lowerscore of 82% still indicates that overall theinstructional routines were followed the ma-jority of the time. The lower score may bereflective of the flexibility that was empha-sized during the training. Because of the het-erogeneity and behavioral issues that are oftentypical of students with ASD, we wanted tomake sure that the tutor felt empowered witha certain level of flexibility to address theuniqueness of each student.

Limitations and Implications for Research

There are several limitations to be consideredwhen interpreting the results of this study.First, the tutor was very experienced with boththe word study program that was adapted andwith working students with ASD. The rapportbetween the students and the tutor may haveresulted in higher outcomes and may also ex-plain why little to no behavior management

issues were reported. Future studies shouldconsider use of school-based personnel in-cluding paraprofessionals that would likely beutilized for providing one to one interven-tions. Second, the length of the interventionwas short considering the severity of students’word reading difficulties. To overcome stan-dard scores that are on average 2.0 SD belowthe normative sample on the TOWRE, stu-dents would most likely need intensive inter-ventions of 80 sessions or more (Wanzek et al.,2013). An intervention with longer durationshould also consider adding more distal out-comes measures such as weekly probes andpre/posttest standardized measures. Third,due to time constraints, we were unable tosystematically capture social validity data fromthe students’ perspective. While the tutor didreport that students appeared to enthusiasti-cally participate, not having a social validitymeasure limits the external validity of thestudy. Finally, while we did not have access toassessment data defining the samples with re-spect to executive functioning variables (e.g.,self-regulation, attention, memory), future re-searchers may want to consider how customizinginterventions for students with ASD to align withtheir basic reading processes or executive func-tioning needs might influence students wordreading outcomes.

Implications for Educational Practice

This study provides evidence for the value ofadapting previously developed programs inorder to increase the intervention’s intensityvia smaller group size, lengthening interven-tion sessions, and developing an individual-ized instructional scope and sequence basedon detailed assessment of previous perfor-mance. School personnel should considerthese adaptations as an alternative to the useof separate programs that may incidentallycause confusion because of differences ininstructional approach and the sequence ofskills taught. With systematic and explicitinstruction, students with ASD with low wordreading skills in the upper grades can im-prove their word reading ability. Practitio-ners should keep in mind the intensity nec-essary to fully remediate word readingdeficits and should consider providing moreintensive interventions.


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Comparing Different Delivery Modes for Literacy-BasedBehavioral Interventions during Employment Training for

College Students with Developmental Disabilities

Kalynn Hall PistoroColumbus State University

Michael P. Brady, Kelly Kearney, andAngelica Downey

Florida Atlantic University

Abstract: Recent research has shown the effectiveness of literacy-based behavioral interventions (LBBIs) as aninstructional strategy for a host of skills and routines, including employment skills. This study compared theeffects of three LBBI storybook formats (print, e-book, and e-book enhanced with video clips) on the accuracy andindependent completion of new employment skills in college students with developmental disabilities. Compar-isons of LBBI formats were made across office tasks including filing papers and reports, using an office copier,and answering a telephone and taking a message. All three LBBI formats were effective in increasingacquisition and maintenance of students’ employment skills. When the formats were compared directly, theenhanced e-book was most effective, followed by the e-book and paper book delivery modes. However, thiscomparison differed somewhat across specific indicators used to establish differential effectiveness, suggestingthat tasks and students also influence the effectiveness of the LBBI formats.

Individuals with developmental disabilities (DD)regularly experience difficulties learning andgeneralizing new skills, and these difficultiesfrequently result in a lack of meaningful, com-petitive employment opportunities (Kaye,Jans, & Jones, 2011). As a result, individualswith DD often require various forms of sup-port to acquire skills needed for employment(Brady & Rosenberg, 2002; Carter, Austin, &Trainor, 2012). Typically these supports areneeded to assure success in academic, employ-ment, and community settings (Cihak, Kes-sler, & Alberto, 2008; Douglas, Ayres, & Lan-gone, 2015; Van Laarhoven, Johnson, VanLaarhoven-Myers, Grider, & Grider, 2009).

For many people with developmental disabil-ities, employment supports are available only ona limited basis as a curricular option in second-ary schools (Guy, Sitlington, Larsen, & Frank,

2009), or in post-secondary community settingsthat offer employment preparation experiencesthrough human service agencies (e.g., Voca-tional Rehabilitation; Wehman, Chan, Ditch-man, & Kang, 2014). Because opportunities foremployment training has been limited, theseinterventions are often more intensive and re-quire additional supports to be effective (Mar-shall et al., 2014), with interventions highly fo-cused on providing coaching, feedback, andmonitoring of individual work performance(Marshall et al., 2014; Wehman et al., 2014).

The advent of postsecondary education pro-grams for adults with DD is a relatively newalternative as an employment training and ed-ucation option. Adults with DD are increasinglyable to attend college to further their education,improve their quality of life, and attain mean-ingful employment (Grigal, Hart, & Migliore,2011; Stodden & Mruzek, 2010). These benefits,in turn, give students access to higher wages,health and monetary benefits, and higher jobsatisfaction (Test et al., 2009). In spite of theselaudatory goals, there is a great deal of variabilityacross most postsecondary education institu-tions in the structure and content of their pro-grams (Grigal, Hart, & Weir, 2012), and a com-mon standard for developing a robust technology

This research was funded in part by a grant fromthe TAFT Foundation to the Florida Atlantic Univer-sity Academy for Community Inclusion. Correspon-dence concerning this article should be addressed toKalynn Hall Pistoro, Teacher Education Department,Frank D. Brown Hall Office 3340, Columbus StateUniversity, 4225 University Ave., Columbus, GA 31907.E-mail: [email protected]


LBBI Delivery Modes / 299


of postsecondary training has yet to evolve (Gri-gal, Hart, & Weir, 2011; Hart & Grigal, 2010).

One strategy that has been used to teach em-ployment skills in postsecondary education andsupported employment programs includes litera-cy-based behavioral interventions (LBBIs). LBBIis an umbrella term for a class of interventionsthat uses print, visuals, and guided rehearsal in astory format to teach new skills and routines(Bucholz & Brady, 2008). Some examples ofLBBIs are Social Stories™ (Gray, 2000), socialscripts (Krantz & McClannahan, 1998), pictureactivity schedule (Spriggs, Gast, & Ayres 2007),and other interventions that provide opportuni-ties for rehearsal following instruction with printand pictures (Weiss & Harris, 2001). One recentformat for LBBIs involves a task analysis used tocreate a story with instructions and pictures witha personal point of view. This story forms themedium used with an instructional package con-sisting of a read, point, model, practice, andpraise format. This LBBI method has been suc-cessful for teaching a variety of skills includingdaily living (Brady, Hall, & Bielskus-Barone,2016), safety (Kearney, Brady, Hall, & Hons-berger, 2017), self-care (Brady, Honsberger, Ca-dette, Honsberger, 2016), self-regulation (Hall,Brady, & Morris, 2017), and job skills (Bucholz,Brady, Duffy, Scott, & Kontosh, 2008).

LBBIs showed particular promise as a sup-ported employment intervention in a pair ofexperiments by Bucholz et al. (2008) whereinvestigators created LBBIs to teach employ-ees to request additional work supplies. In thefirst experiment, two women aged 26 and 48with IQs ranging from 29 to “below 59” par-ticipated in a work improvement plan as aresult of a long-term decline in their produc-tivity. To increase their productivity, an LBBIwas created teaching them to request (a) as-sistance, (b) more work supplies, and (c) abreak from work as appropriate. As a result ofthe intervention, both women significantly in-creased their requests and work productivity.In addition, the co-workers closest to the em-ployees also increased their productivity. Inobservations conducted nearly three monthsafter the intervention was removed, these im-provements were still evident. In the secondexperiment, a 57-year-old man with Down syn-drome received an LBBI to decrease transi-tion time between breaks and work. The LBBItargeted returning from breaks and decreas-

ing the levels of prompts needed by an escort.Results showed a decrease in the need forprompts by co-workers, and an increase inindependent and timely returns to work. In asubsequent study with four adolescents withmoderate to severe autism (Brady, Honsbergeret al., 2016), an LBBI delivered by peers wasused to teach a food preparation skill. All fourstudents learned the task in 8–12 sessions andshowed maintenance of these skills on fol-low-up probes delivered up to 30 days after theremoval of the intervention.

As the research with LBBIs has gained interestas an intervention for secondary students andadults, investigators have questioned whethercreating different story delivery formats andimplementing the intervention with technol-ogy might improve LBBI outcomes even fur-ther (Brady, Hall et al., 2016; Kearney et al.,2017). Traditionally, LBBIs have been deliv-ered as paper-based written text, in short sto-ries, or as scripts. However, implementing thestorybook strategy with electronic visual media(such as portable electronic devices and tab-lets) might make the interventions more ac-cessible and appealing to students and adults(Mechling, 2008), potentially increasing theiruse during instruction in community employ-ment settings. These alternate media formatsalso have the ability to conjoin video model-ing, auditory prompts, and written scripts. Forexample, one form of video modeling breaksa task into separate steps and video recordseach task (Mechling, Ayers, Bryant, & Foster,2014). If similar adaptations are applied toLBBIs, it is possible that this class of interven-tions could expand its efficacy in ways that par-allel the expansion of video modeling or audioand video prompting (Cihak et al., 2008; Doug-las et al., 2015; Van Laarhoven et al., 2009). Todate, applications of technology have beenmissing from the research on LBBIs.

Rationale for the Current Study

The purpose of the study was to compare threedifferent LBBI delivery modes for teaching em-ployment skills to adults with developmental dis-abilities. Specifically, we investigated the effec-tiveness of an instructor delivered paper-basedbook, an e-book, and an e-book enhancedwith video clips on the job skill acquisition ofcollege students in a postsecondary program


for adults with developmental disabilities. Tworesearch questions were posed:

1. Are there differences in job skill acquisi-tion by adults with developmental dis-abilities based on the mode of delivery ofa literacy-based behavioral intervention?

2. Are there differences in the mainte-nance of newly learned job skills basedon the LBBI delivery mode?

Method

Participants

Three adults attending a postsecondary col-lege program for people with developmentaldisabilities, all aged 22, participated in the study.All were in the first year of their program, andwere enrolled in specialized academic courseswith employment exploration experiences. Allhad reading comprehension levels at a fourthgrade equivalence. None of the students hadprior office work experience although one hadpreviously worked for a grocery store, and asecond previously worked in a warehouse pre-paring items for shipping. All students assentedto participate, and their parents provided con-sent prior to initiating the study. Participantcharacteristics are summarized in Table 1.

Task and Setting

Three job skills were selected based on (a) stu-dents’ career interests, (b) their job coach’s rec-ommendations, and (c) routines required intheir employment settings. These skills included(a) filing papers and reports in folders and thenplacing them in filing cabinets, (b) copying pa-pers using an office copier, and (c) answeringan office telephone and taking a message. Priorto selecting each task, students demonstratedthat they were physically capable of performingeach task. Instruction for all three tasks tookplace on the university campus in the job coach-ing office. This is also where students performedthe phone and filing tasks. For the copying task,students performed the task in a work roomwith an office copier located just down the hallfrom the job coaching office.

Behavioral Measure, Data Collection, andInter-Observer Agreement

A task analysis for each of the three job skillswas developed and served as the behavioralmeasure for each student. Each task analysiscontained 10 to 11 steps and, in turn, was usedto construct the LBBI story that formed the basis

TABLE 1

Participant Characteristics

Student Assessment Data Disability Reading Level Prior Office Experience

Marty WAIS: 61 ID; ASD Recognition grade 8;Comprehensiongrade 4

NoneJOBS: OSDPerformance � 84Support � 80

Karen WAIS: 50 ID; OHI(ADHD)

Recognition andComprehensiongrade 4

None; worked at awarehouse preparingitems for shipping

JOBS: OSDPerformance � 84Support � 80

Diane KBIT-2: 48 ID (Down’sSyndrome)

Recognition andComprehensiongrade 4

None; worked at agrocery store as abagger and bakeryassistant

OBS: OSDPerformance � 84Support � 80

Note: WAIS � Full Scale IQ score from the Wechsler Adult Intelligence Scale-IV (Wechsler, 2008). KBIT-2 �Composite IQ from the Kaufman Brief Intelligence Test- Second Edition (Kaufman & Kaufman, 2004). JOBS: OSD � JobObservation and Behavior Scale: Opportunity for Self Determination (Brady, Rosenberg, & Frain, 2006). For adults incompetitive, supported, and sheltered employment, the mean composite score for Quality of Performance �77.99; the mean composite score for Type of Support � 76.17. ID indicates Intellectual Disability; ASD indicatesAutism Spectrum Disorder; OHI indicates Other Health Impairment.


of instruction. Data were collected for each stu-dent individually by observing the student per-forming the skill, and then scoring each step inthe task analysis as (a) correct and independent,(b) correct with prompts, or (c) incorrect.

A correct and independent step was de-fined if the student performed the step inthe task analysis without assistance. Correctwith prompts was recorded if the student re-ferred back to the book to get information onhow to complete a step, or required a direc-tion to seek that information from the book.An incorrect response was recorded if stu-dents made errors that they were unable tocorrect themselves, if they did not complete astep in the task analysis, or if they omitted astep. Steps had to be performed in a strictsequential order only if required for the skillto be completed. For example, when makingcopies students could put the papers in thefeeder first, or they could enter the passwordfirst; the order of these particular steps wasirrelevant to performing the whole task. How-ever, when making copies, students had to putthe password into the copier before enteringthe desired number of copies. Only steps thatwere both correct and independent were usedfor instructional decisions and included in thegraphed results.

Data were collected by observers stationedapproximately five feet away from the stu-dents, yet close enough to see and hear themcarry out the task. Observers were graduatestudents and experienced special educationteachers. Observers were trained to criteriaon the data collection system and practicedusing the data sheets prior to the study. On35% of the sessions two observers recordedthe college students’ performance simultane-ously for purposes of assessing inter-observeragreement. This accounted for 35% of thesessions for Karen, 38% of sessions for Marty,and 32% for Diane. Across the tasks, this ac-counted for 43% of the observations for thefiling task, 42% of the observations for copy-ing, and 31% of the observations for answer-ing the phone. Agreement was determined bycounting the steps of the task analysis scoredthe same by both observers, and dividing thatnumber by the total number of steps observed,then multiplying by 100. Total agreement acrossall participants for all three task analyses wasgreater than 99%, with only one session not

having 100% agreement. (Agreement on Di-ane’s filing was 91% for one session.)

Development of the LBBI Stories

After each of the job skills was task analyzed, astory was constructed depicting how to com-plete the steps successfully. For filing therewere 10 steps in the task analysis, with ninepages plus a front cover; in this book two ofthe steps were combined on one page. Thecopying task analysis also had 10 steps and a10-page story, plus a front cover (each step wasgiven its own page in the copying book). Thetask analysis for answering the telephone was11 steps, but the book was only 10 pages plusa front cover; two steps were combined on onepage. All of the stories were created in Power-Point and the accompanying visuals were pre-sented from a personal point-of-view, usingguidelines described by Schreibman, Whalen,and Stahmer (2000) and implemented in otherLBBI research (Brady, Hall et al., 2016; Brady,Honsberger et al., 2016; Hall et al., 2017).

For the paper book and e-book the samePowerPoint presentation was used. The onlydifference between these two story formatswas whether the book was presented in printformat in a 3-ring binder, or on an i-Pad in thePowerPoint application. The accompanyingvisuals for the paper book and e-book wereapproximately 3x4 inches. For the enhancede-book the same presentation was used but thepictures were replaced with a short video clipof the step, also presented from a personalpoint-of-view. The videos were approximatelythe same size as the pictures and lasted be-tween two and 14 seconds, with the averagevideo clip lasting approximately four seconds.Each page of the three book formats containedone to two sentences at the top of the pagedescribing the step portrayed in the picture orvideo. Sentences contained between three and16 words, with an average of eight words. Asample page from a story is found in Figure 1.

Experimental Procedures

Baseline. During baseline, an investigatorin the job coach’s office asked individual stu-dents to “file this paper”, “make three copies”,and “answer the phone and take a message ifit rings”. Each request was given independently


and students were given 30 seconds to completeeach step of the task analysis. If students saidthey did not know what to do next, if they com-pleted the task with errors, or if 30 secondstranspired without completing a step, the ses-sion was stopped. During all baseline sessions,the paper book, e-book, or enhanced e-bookassigned to the student for the task was present,but no prompts were given to the student to useor not use the storybook to assist with the task.

Preparation for intervention. Prior to inter-vention, students were randomly assigned to adifferent LBBI presentation format for one ofthe three employment tasks. Following theassignment for the first presentation modeand task, the delivery formats for the remain-ing tasks were counterbalanced across the stu-dents so that each student was assigned eachof the three formats across all three tasks. Thatis, the LBBI delivery mode differed for eachstudent for the filing task, the copying task, and

the telephone answering task. A summary of thedelivery formats and employment tasks acrossthe three students is found in Table 2.

Intervention. The LBBI was implementedindependently for each student. Studentswere provided with the intervention for eachtask once per day, for 4 to 5 days each week.During the intervention, an investigator andstudent sat at a table in the job coaching office(the same office used during baseline) withthe assigned book displayed in front of them.The student read each page of the story aloud,and an investigator instructed the student torole play the skill using a story instructionprocedure (i.e., read, point to, pause, prac-tice, and provide reinforcement for each stepthat was completed correctly). For each pageof the story, while the investigator and studentwere still in the job coach’s office, the investi-gator asked the student to point to the pictureor play the video, then practice what they were

Figure 1. Sample page from a Literacy-Based Behavioral Intervention storybook on filing papers and folders.

TABLE 2

Arrangement of Tasks and Delivery Modes across Students

Students Filing Copying Phone

Karen Enhanced e-book Paper book E-bookMarty E-book Enhanced e-book Paper bookDiane Paper book E-book Enhanced e-book


going to do. Each step used a simulated prac-tice procedure; for example, students pushedtheir thumbs in and pulled their arms back tosimulate opening the filing cabinet drawer.Finally the investigator reinforced this prac-tice with verbal praise.

As soon as the student finished reading thestorybook, the investigator asked him or herto complete the actual task using the samescript as in baseline. Therefore, the delay be-tween the LBBI session and a student’s oppor-tunity to respond independently was negligi-ble. For the filing and answering the phonetask the student remained in the job coach’soffice to complete the task; for the copyingtask the student had to go across the hall intothe copy room. While the student completedthe task, the investigator stood in the doorway,approximately five feet away. The book ortablet remained on the table open and avail-able to the students for reference if needed. Ifthe student referred to the book, the step wasmarked as correct with prompts, or as an er-ror. If students asked for assistance with a step,they were referred to the book for directions.As in baseline, if the student did not initiate astep in the task analysis within 30 seconds ofcompleting the previous step, the investigatorreminded the participant to do what she or hehad seen previously in the storybook, and aprompt was recorded. No other peers oradults provided instruction with or withoutthe students’ storybooks.

Follow-up. After students showed mastery(defined as 100% of steps completed correctlyand independently for five of six sessions), orif a student had five consecutive stable sessionswith no additional progress, the interventionwas stopped and the LBBI was removed to seeif the gains would maintain without the inter-vention. During these follow-up observations,baseline procedures were replicated; studentsno longer read the story to an investigator orreceived prompts to refer to the storybooks.That is, an investigator asked each student tocomplete the various work skills, but no assis-tance or intervention was provided.

Experimental Design

The experimental design in this study com-bined a multiple probe design across worktasks with a variation of the adapted alternating

treatments design (Wolery, Gast, & Ledford,2014). This design arrangement followed themodel used by Mechling et al. (2014) that al-lowed for a simultaneous analysis of differ-ences across participants, tasks, and interven-tion delivery modes. The multiple probedesign established a functional relation be-tween the LBBI and the job skills performedby each student. The adapted alternatingtreatments design showed the comparative ef-fects of the LBBI delivery types (paper book,e-book, and enhanced e-book) presented tothe students across the various tasks. To avoidsequence effects, the three delivery modeswere counterbalanced across the work tasksand students. After the intervention condi-tion, follow-up observations were conductedfor each student and work task to evaluatewhether the newly learned skills would main-tain in the absence of the LBBI. For the filingtask, the first follow-up observations were held7, 9, and 13 days after the last day of interven-tion for, Marty, Diane, and Karen respectively;the second follow-up observations were held 6days after the first observation. For the copy-ing task, the first follow-up observation wasconducted 7, 8, and 9 days after the last dayof the students’ intervention; the secondfollow-up session was 6 days after the first. Fi-nally, for phone use, the first follow-up observa-tion was conducted 12 days after interventionfor Karen, and 6 days after the intervention forMarty and Diane. The second follow-up sessionfor phone use was conducted 7 days after thefirst.

Data Analysis

Data were analyzed using visual inspectionprocedures typically used for single subjectdesign studies. To assess the impact of theLBBI on students’ acquisition of the skills, wecalculated the means for each student’s de-pendent measure during baseline, interven-tion, and follow-up. In addition, we examinedranges for each condition, and examined con-dition changes based on the level and trendsof individual data points. We used these samedata analysis procedures to compare results ofthe three delivery modes. For this comparisonof delivery modes, we examined the dataacross the particular skills and students. Thisenabled us to examine changes regardless of


student and skill differences from (a) baselineto intervention, and (b) baseline to follow-upfor each delivery mode.

We also supplemented these visual inspec-tion procedures with an effect size estimateduring a post-hoc analysis. To establish effectsizes, we calculated the Percent of Non-Over-lapping Data (PND; Scruggs & Mastropieri,2013). Although there is little consensus re-garding effect size protocols for single subjectstudies (Ledford, Wolery, & Gast, 2014), PNDhas been the most common effect size esti-mate across three decades because it showsconsistency of effects across participants, andis appropriate for conditions that do not showdata outliers. For this study PND was calcu-lated separately for baseline-to-intervention,and baseline-to-follow-up conditions.

Results

The effects of the LBBI, as well as the differ-ences in job skill acquisition and maintenancein the three college students based on deliverymode of the LBBI, are shown in Figure 2 andthe top half of Table 3. Overall, the LBBI waseffective in increasing students’ acquisition ofthe skills, regardless of the particular skill,delivery mode, or student. When examiningthe student acquisition for each task, Figure 2shows that for the filing task (top graph),none of the students completed more than20% of the steps correctly and independentlyduring baseline. When the LBBI was imple-mented all three students increased their ac-curacy and independence in performing thefiling task, although only two of the threestudents actually showed mastery of the skill.For the copying task (middle graph), the samepattern was seen, with low levels of accuracy (0to 20%) during baseline, followed by immedi-ate acquisition when the LBBI was imple-mented. Baseline for the telephone answeringtask (bottom graph) was more variable, withstudents’ performance ranging from 9% to38%. However when the LBBI was imple-mented, two of the students immediately dem-onstrated mastery, while the third student(Marty) required 4 days to achieve at least90% of the steps. When the LBBI was removed,students maintained the same performance lev-els during their follow-up observations as theydid during their final intervention sessions.

Although the LBBI was seen as having aneffect on each student and job task, the studyalso sought to establish whether there wouldbe differential effects of the interventionbased on the three modes of delivery of theLBBI. To examine the impact of each deliverymode, the top half of Table 3 shows that eachdelivery mode resulted in substantial gain.When all tasks are combined, the meansacross the delivery modes from baseline tointervention ranged from 51% to 81% im-provement. Substantial gains were also seenwhen comparing baseline to the follow-upcondition, with means ranging from 61% to84% improvement. In each case the impact ofthe e-book and enhanced e-book were similar,and outweighed the impact of the paper book.Among the individual tasks, the apparent ef-fectiveness of the enhanced e-book overall wasseen for the filing and copying tasks, but was 5percentage points less than the e-book deliv-ery for answering the telephone.

A second indicator of the differential effec-tiveness of the delivery modes is found byexamining the percentage of sessions in whichstudents performed the new tasks at a masterylevel of 100% accuracy and independenceduring the intervention condition. When alltasks are combined, the enhanced e-booktasks resulted in the largest percentage of in-tervention sessions with mastery (83%), fol-lowed by the e-book (68%) and paper book(24%) delivery modes respectively. Amongthe individual tasks shown in Table 4 (seconddata column), this pattern was seen for thefiling and copying tasks, but the e-book deliv-ery mode showed the highest percentage ofmastery sessions (100%) among the threemodes for answering the telephone. Next, thenumber of intervention sessions needed for astudent to reach 100% accuracy and indepen-dence on a task (first data column in Table 4)was examined as a third indicator of differen-tial effectiveness. Students who received theenhanced e-book delivery mode reached thisperformance level quicker on two of the threetasks (filing and copying). For the telephonetask, students using the e-book and enhancede-book had an equal number of interventionsessions to reach mastery. For all three deliv-ery modes, students who received instructionwith the paper book took longest to reach thisperformance level.


Finally, the Percent of Non-OverlappingData analysis indicted that, regardless of stu-dent and skill differences, the LBBI was effec-

tive to very effective for all three LBBI deliverymodes based on standards described by Led-ford et al. (2014), and Scruggs and Mas-

Figure 2. Effects of three LBBI delivery modes across students’ employment tasks.


tropieri (2013). This was observed (a) frombaseline to intervention, and (b) from base-line to follow-up. Only the paper book versionof the LBBI did not result in a very effectivefinding. These results are found in the bottomhalf of Table 3.

Discussion

Each of the students who participated in theemployment training increased their accuracy

and independence on three different tasksduring this study, and showed substantialprogress toward mastery on these tasks. Whenthe LBBI was removed, each student contin-ued to perform the new skills during the fol-low-up observations nearly three weeks later.Just as important, each student showed learn-ing gains regardless of the LBBI delivery for-mat that was used. While the learning gainsacross multiple tasks and delivery formatswere encouraging, it was equally important tonote that the effect of the LBBIs on learningwas rapid, regardless of the format used todeliver the intervention. Of the six demonstra-tions of effect using the e-book or the en-hanced e-book delivery format, studentsshowed their mastery of the employment skillsafter six or fewer intervention sessions, andseveral demonstrated immediate mastery dur-ing the first intervention session. The use ofthe paper book was more variable. Althoughall three skills showed rapid improvementswithin two to four LBBI sessions, one of thestudents never achieved mastery on one of theskills (Diane achieved 60% accuracy and inde-pendence on the filing task), and a secondstudent (Marty) achieved only 91% of the tele-phone skills. Taken together, these findingsstrengthen the evidence base for the use ofLBBIs as an instructional strategy, particularlyfor teaching employment skills. These find-ings are consistent with previous LBBI investi-gations (Brady, Hall et al., 2016; Brady, Hons-berger et al., 2016; Bucholz et al., 2008; Hall

TABLE 3

Performance Differences and Effect Sizes (PND)Across Delivery Modes

Delivery ModeBaseline to

InterventionBaseline toFollow-Up

Mean Changes AcrossConditions

Paper bookMean 51% 61%Range 29–82% 40–87%

E-bookMean 78% 84%Range 67–84% 80–90%

Enhanced E-bookMean 81% 84%Range 75–90% 80–90%

Percentage of Non-Overlapping Data

Paper book 90% 100%E-book 100% 100%Enhanced E-book 100% 100%

Table 4

Arrangement of Tasks and Delivery Modes across Students

Delivery ModeSessions Neededto Reach 100%

Intervention Sessionsthat Reached 100%

Mean Increaseover Baseline

FilingPaper book Did not reach 100% 0/7 � 0% 29%E-book 6 5/10 � 50% 67%Enhanced e-book 2 5/6 � 83% 95%

CopyingPaper book 2 5/6 � 83% 82%E-book 3 5/7 � 71% 84%Enhanced e-book 1 5/5 � 100% 90%

TelephonePaper book Did not reach 100% 0/8 � 0% 67%E-book 1 5/5 � 100% 82%Enhanced e-book 2 5/7 � 71% 77%


et al., 2017; Kearney et al., 2017), but extendthe intervention to electronic delivery formatsfor the storybooks – formats that parallel theprogress made with video modeling andprompting interventions during the last de-cade (Cihak et al., 2008; Douglas et al., 2015;Mechling, 2008; Mechling et al., 2014; VanLaarhoven et al., 2009).

Limitations

As with all research there were limitations inthis study. Because all participants were in thesame post-secondary program and knew oneanother, it is possible that they might havediscussed the skills they were learning, inad-vertently providing support to their peers whowere also receiving instruction on the officeskills. In addition, since all data were collectedin an active office area on campus, the stu-dents could have observed actual employeesperforming the skills, and thus gained unin-tended opportunities for observational learn-ing.

Finally, we evaluated the differential effec-tiveness of the three delivery formats usingthree different indicators of effectiveness. Theresults did not unanimously support any sin-gle format as “best”, but did support the elec-tronic media formats as modestly more effec-tive than the paper books. When comparingthe different effects of similar treatments, it isnecessary to consider whether differences inthe results are large enough to be meaningful,and whether the differences are consistentacross participants (Wolery et al., 2014). Inthis study it was clear that all of the LBBIformats were effective, although the differen-tial effects were not always robust. Althoughthe paper book intervention resulted ingrowth for all students, only one achieved cri-terion for mastery. Perhaps the lack of addi-tional supports, such as the ability to zoom inand out of the picture to see details, contrib-uted to this difference. Another possibility isthat students might not have been as moti-vated to use the paper book as they were touse the i-Pads; this motivation differencecould have resulted in decreased learning, aphenomenon observed in numerous otherstudents (Clampa, 2014). In contrast, the e-book and enhanced e-book both resulted inall students reaching mastery across the vari-

ous office skills and routines. While the varia-tion between the e-formats is less apparent,students mastered all skills more quickly whileusing the enhanced e-book. Perhaps the addi-tion of a video model or video prompt con-tributes to this difference. Video modelingand prompting have been repeatedly demon-strated as effective interventions for peoplewith developmental disabilities, across a varietyof media, formats, and technologies (Cihak etal., 2008; Lasater & Brady, 1995; Mechling &Gustafson, 2009; Van Laarhoven et al., 2009).Future research might include more partici-pants and target skills to identify whethergreater differences might be discovered as moreexperimental trials are run.

Implications for Research

Similar to previous investigations of LBBI effi-cacy as an instructional strategy, all three inter-vention formats in this study incorporateddiscrimination training, imitation, prompting,rehearsal, and visual models into the instruc-tional procedures. Because there has been noattempt to isolate the effects of these instruc-tional components, the LBBI in this and pre-vious research is best described as an instruc-tional package that incorporates elements ofwell-designed explicit instruction (Gersten,Carnine, & Woodward, 1987). Complex skillsand routines are task analyzed, and each pageof the story teaches an important part of thelarger skill. Written information is providedalong with verbal instructions and visual mod-els of how the skills should be performed. This“story” provides students with a structuredskill sequence to teach them to perform theskills and routines by following the pages oftheir paper and electronic books. The find-ings in this study do not establish whethereach component of the LBBI is necessary. Asfuture LBBI studies continue to expand thestudent populations, learning outcomes, anddelivery formats, it might be helpful toconduct a component analysis to establishwhether each of these elements is needed. Forexample, all of the components of the LBBIsin this investigation might not fit the structureof some college programs if the studentsin those programs do not have didacticcourses, with employment experiences andjob coaches who implement explicit skill train-


ing (Grigal et al., 2012; Hart & Grigal, 2010).Students in the current study benefitted froma college program structure that provideddaily supported employment training for ev-ery student with developmental disabilitieswho enrolled in college. In addition, as futurestudies examine the different delivery modes,it will be important to examine the impact ofthe various LBBI components on these deliv-ery modes.


As more adults with developmental disabilitiesseek education and employment trainingthrough college and other postsecondary ex-periences, the need for effective interventionswill continue to grow. Literacy-based behav-ioral interventions appear to be one such in-tervention. Like other college learners, in-structional formats that incorporate mobileelectronic devices, tablets, and other portablevisual media appear to be effective methods ofpromoting knowledge and skill developmentin college students with developmental dis-abilities. The results of this study, combinedwith the settings and skills in previous re-search, suggest that LBBIs with electronic me-dia could be effective with a variety of othercommunity tasks. For example, teachers, jobcoaches, and other professionals might useLBBIs for residential living tasks, other em-ployment outcomes, leisure skills, or to other-wise promote self-determination. As such,LBBIs are emerging as a promising evidence-based practice.

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Received: 27 April 2017Initial Acceptance: 26 June 2017Final Acceptance: 10 August 2017


https://doi.org/10.1177/0145445517725866

Electronic Essay Writing with Postsecondary Students withIntellectual and Developmental Disabilities

Suzanne Woods-GrovesUniversity of Iowa

Saeed S. AlqahtaniPrince Sattam Bin Abdulaziz University

Kinga Balint-LangelUniversity of Minnesota Duluth

Amanda KernUniversity of Nebraska Omaha

Abstract: This study examined the efficacy of an electronic essay-writing strategy to improve the expositorywriting skills of 20 young adults with intellectual and developmental disabilities enrolled in a program at aninstitute of higher education in the midwest. A pretest and posttest experimental design with random assignmentto treatment or control group was used to investigate the mnemonic-driven electronic writing strategy. Thewriting strategy supported students’ construction of essay responses using a computer. Students used the strategyand a computer word program to examine an electronically presented essay test question, plan through theconstruction of an electronic outline, and create and revise an electronic essay response. Pretest and posttest essayresponses were evaluated through proximal and distal rubrics. Results revealed a significant positive effect forthe treatment group when compared to the control group for overall essay quality including use of ideas andcontent, and word choice.

Writing is a complicated process and an inte-gral form of communication. Graham andPerin (2007) asserted that “along with readingcomprehension writing skill is a predictor ofacademic success and a basic requirement forparticipation in civic life and the global econ-omy” (p. 3). The National Commission onWriting (2006) report Writing and School Re-form asserted that difficulties in writing canserve as a barrier in the areas of communica-tion, achievement in school, and success inthe workplace. The National Assessment ofEducational Progress (NAEP, 2011) comput-er-based writing assessment results for second-ary students who were administered the NAEPrevealed 74% of eighth graders and 73% of

12th graders fell in the below proficient range(National Center for Education Statistics,2012). Students with and without disabilitiesexperience difficulty in writing. These difficul-ties if not addressed can impede one’s successin postsecondary education programs. As in-dividuals with intellectual and developmentaldisabilities (IDD) exit high school they havethe option of attending postsecondary educa-tional programs at institutions of higher edu-cation (IHEs). Currently there are approxi-mately 246 college programs for individualswith IDD (Think College, 2016).

The writing process is iterative in natureand includes metacognitive processes of plan-ning, organizing, constructing text, and re-vising (Hayes, 1996, 2012; Hayes & Flower,1980). Difficulties in the writing process canoccur in planning, organizing, constructingtext, and revising (Mason & Graham, 2009;Pennington & Delano, 2012). It is recom-mended that evidence-based practices be em-ployed in writing practices (Cook et al., 2015;Odom et al., 2005; What Works Clearing-house, 2013). Replications and extensions ofinstructional practices that reveal positive ef-fects for learners’ writing skills are one aspect

The research reported herein was supported inpart by the Office of Postsecondary Education(OPE), U.S. Department of Education, throughGrant P407A100030 to The University of Iowa. Theopinions expressed are those of the authors and donot represent views of the OPE or the U.S. Depart-ment of Education. Correspondence concerning thisarticle should be addressed to Suzanne Woods-Groves,Special Education, Rehabilitation, & Counseling, 2084Halely Center, Auburn University, Auburn, AL 36849.E-mail: [email protected]


Electronic Essay Writing / 311


of assessing effective practices (Graham, Har-ris, & Chambers, 2016). Graham et al. (2016)systematically evaluated 19 previously con-ducted reviews of writing instruction studiespertaining to K-12th grade learners with andwithout disabilities. Graham et al. (2016) re-sults supported but were not limited to thefollowing practices: (a) the incorporation ofword processing with supports for 4 first- tofifth-grade students yielded a large effect size1.46; (b) the use of writing goals for fourth-to eighth-grade students yielded a large effectsize .80; (c) writing strategies that employedan explicit instruction framework was sup-ported for 84 studies across second- to 12th

grade students and yielded effect sizes thatranged from 56 to 1.59; (d) use of feedbackfrom adults (i.e., seven studies) or peers (i.e.,10 studies) yielded effect sizes that rangedfrom .77 to .87; and (e) the use of self-assess-ment for 13 studies second- to 12th graderswith an effect size of .51.

The majority of writing instruction investi-gations have been conducted in K-12th gradesettings with students without disabilities andstudents with disabilities including studentswith learning disabilities, autism spectrum dis-orders, behavioral problems, intellectual dis-abilities, and students with attention deficithyperactivity disorder (De La Paz, 1999; Del-ano, 2007; Englert, Raphael, Fear, & Ander-son 1988; Graham & Harris 2003; Graham etal., 2016; Graham, McKeown, Kiuhara, & Har-ris, 2012; Joseph & Konrad, 2009; Lane et al.,2009; Pennington & Delano, 2012). For thosestudents with disabilities who leave highschool and enter postsecondary IHEs with dif-ficulties in writing, it is imperative to provideevidence-based instruction in the area of writ-ing. Graham and Perin (2007) noted that 75%of writing tasks are expository in nature for12th graders and that the majority of collegeclassroom writing tasks are expository. Withthe increase of individuals with IDD who areentering postsecondary programs at IHEs ex-pository writing strategies should be availablefor students who need them. Several investi-gations have been conducted with college-agestudents with IDD and an essay writing strat-egy.

An essay writing strategy instruction devel-oped by Hughes, Schumaker, and Deshler(2005), the Essay Test-Taking Strategy, is mne-

monic-based, and incorporates explicit instruc-tion. This six-step strategy uses the ANSWERmnemonic to guide students in analyzing essayquestions, planning what to write, constructingan essay response, and revising/reviewing theessay response. The ANSWER mnemonic repre-sents the following steps: (a) Analyze actionwords in the essay question, (b) Notice the re-quirements of the essay question, (c) Set up anoutline, (d) Work in outline details, (e) Engi-neer an answer, and (f) Review the answer.

The Essay Test-Taking (i.e., ANSWER)Strategy has been previously investigated infour experimental group-design studies de-signed to assess the utility of the strategy toimprove students with disabilities’ expositoryessay-writing skills. Therrien, Hughes, Kapel-ski, and Mokhtari (2009) studied the use ofthis strategy with 40 seventh- and eighth-gradestudents with learning disabilities. The partic-ipants were randomly assigned to ANSWERintervention (n � 21) or non-intervention(n � 19) groups. The ANSWER interventionwas conducted for eight 35-minute sessions.Students in both groups were administeredpre- and posttests in which they were given anessay test question and asked to construct anessay response. The students’ essay responseswere evaluated by scores obtained through aproximal strategy-specific rubric and a distalholistic analytical rubric. The interventiongroup performed significantly higher than thenon-intervention group in their use of thestrategy steps (i.e., analyzing the essay ques-tion and constructing and outline) measuredby the strategy rubric, and in the organization,ideas, and content of their essay responses(i.e., large effect size of d � 1.69) measured bythe analytic rubric (Cohen, 1988).

A series of three sequential experimentalgroup studies were conducted by Woods-Groves and colleagues in order to investigatethe efficacy of employing the ANSWER mne-monic-driven strategy with postsecondarystudents enrolled in a two-year certificate pro-gram for young adults with IDD (Woods-Groves et al., 2014; Woods-Groves, Therrien,Hua, & Hendrickson, 2013; Woods-Groves,Therrien, Hua, Hendrickson, Shaw, & Hughes,2012). A minimum of 16 young adults withIDD (e.g., students with autism, Asperger’sSyndrome, non-verbal learning disorder, mildor moderate intellectual disabilities) partici-


pated in each study with a cumulative total of51 students across all three investigations. Theauthors indicated that in each study the par-ticipants were randomly assigned to an AN-SWER intervention group or a non-interven-tion group. The intervention groups in eachstudy received large-group instruction in theANSWER strategy and employed the strategyto create handwritten essays. Lessons delin-eated in the Essay Test-Taking Strategy man-ual (Hughes et al., 2005) were adapted toinclude lesson specific graphic organizers butmaintained the explicit instruction format(i.e., modeling, guided practice with correc-tive feedback, and independent practice).The six ANSWER lessons were taught in asequential mastery-based format (i.e., 80%mastery). Students were provided with folderswith the ANSWER mnemonic attached to thefront, graphic organizers, a histogram graphfor self-graphing, and highlighters. The au-thors in each ANSWER study, analyzed rubricresults through an Analysis of Covariance(ANCOVA) where mean posttest rubric scoreswere compared for the intervention and non-intervention groups with the pretest meansemployed as a covariate.

The ANSWER intervention consists of sixlessons that can be taught through a seriesof instructional sessions. Woods-Groves et al.(2012) conducted the first ANSWER studythat consisted of six 30-minute sessions, threetimes a week for 2 weeks for a cumulative totalof 3 hours of instruction. The proximal strategyrubric results indicated students in the inter-vention group significantly outperformed thecontrol group on overall rubric scores butonly on aspects of strategy use (i.e., applyingthe first four steps of the strategy--- analyze theessay prompt and construct an outline), d �2.63, p � .001. The authors found no signifi-cant differences concerning aspects of thequality of the essay constructed (i.e., steps fiveand six of the strategy--- creating the essay andrevising the essay). As a result, the secondANSWER study conducted by Woods-Groveset al. (2013) incorporated individualized writ-ing goals for the intervention group and alonger instructional time for the intervention(i.e., intervention time � 50 minute lessons,two days a week for 3 weeks, for a cumulativetotal of 5 hours). The proximal strategy rubricresults revealed students in the intervention

group significantly outperformed the non-in-tervention group in the following: (a) overallproximal strategy rubric results, d �1.90, p �.002; (b) use of 1–4 strategy steps, d � 1.85,p � .002; and (c) steps 5–6 essay constructionand revision, d � 1.12, p � .019. In 2014,Woods-Groves and colleagues conducted athird investigation of the ANSWER strategy topromote the construction of handwritten es-say responses with young adults with IDD withthe following adaptations: (a) the interven-tion time was further extended to ten 45 min-ute sessions, two days a week for 5 weeks, for atotal cumulative time of 7.5 hours, (b) indi-vidualized student writing goals, (c) a proxi-mal strategy specific rubric and distal holisticanalytic rubric were employed as dependentvariables, and (d) generalization and mainte-nance skills were assessed. Results indicatedthe ANSWER intervention group significantlyoutperformed the non-intervention group inthe proximal strategy specific rubric overallresults, d �8.63, p � .001, strategy specificsteps, d � 15.85, p � .001; and essay construc-tion/revision, d � 1.50, p � .002. The distalanalytic rubric indicated significant results infavor of the intervention group in the areas ofoverall score, d � .95, p �.001, and in thecombined area of ideas/content and organi-zation, d � 1.44, p � .001. According toWoods-Groves et al. (2014) students who re-ceived the ANSWER instruction were assessed2 weeks following the end of the interventionfor generalization and 13 weeks later for main-tenance of skills. In the generalization phasestudents “performed at approximately 54.79%of their previous posttest score level” and inthe maintenance phase students “performedat approximately 63.64% of their previousposttest score level” (Woods-Groves et al.,2014, p. 260).

The previous four experimental investiga-tions of the ANSWER strategy with studentswith disabilities focused on the constructionof handwritten essays. The aim of this currentinvestigation was to examine the efficacy ofthe use of the ANSWER strategy to improvepostsecondary students with IDD’s skill in con-structing electronic-based essays.

The following research questions were in-vestigated:


1. Will postsecondary young adults withIDD employ the ANSWER writing strat-egy when constructing their electronicessay-test responses?

2. Will there be a significant difference inhow postsecondary young adults with IDDuse the strategy specific aspects (Steps 1 –4) and the essay general component as-pects (Steps 5 – 6) of the ANSWER writingstrategy when constructing electronic es-say-test responses?

3. Will there be a significant difference inthe quality of electronic essay responsesfor postsecondary young adults with IDDin the intervention group and those inthe non-intervention group in the ana-lytic rubric areas of ideas/content, orga-nization, voice, word choice, sentencefluency, and conventions, respectively?

Method

Participants

The 20 participants in this study were allyoung adults who were in their second year ofa two-year postsecondary certificate programfor individuals with IDD. The postsecondaryprogram was at a research 1 institute of highereducation located in the midwest. There were(7, or 35%) females and (13, or 65%) maleswho participated in this study and who rangedin age from 18 to 23 years, (M � 19.55, SD �1.36). The participants lived in rural, (8, or40%), urban, (6, or 30%), and suburban, (6,or 30%) demographic areas. Educational di-agnostic information for the participants indi-cated that three (15%) individuals were diag-nosed with autism, one (5%) individual withAsperger’s Syndrome, two (10%) with Perva-sive Developmental Disorder, six (30%) withintellectual disability, one (5%) individual wasdiagnosed with Down Syndrome, four (20%)with other health impairment, two (10%) witha severe learning disability and speech impair-ment, and one (5%) individual’s diagnosis wasnot reported.

Woodcock Johnson Achievement III (WJIII;Woodcock, McGrew, & Mather, 2001) TotalScores for all participants ranged from 20-to-102, Mdn � 74, (standard scores with a M �100, SD � 15), while Broad Reading scoresranged from 30-to-105, Mdn � 79, (standard

scores with a M � 100, SD � 15) and BroadWritten Language scores ranged from 30-to-105, Mdn � 78, (standard scores with a M �100, SD � 15). The participants were stratifiedbased upon their WJIII Broad Reading scoresand were randomly assigned to the ANSWERstrategy intervention group or the non-inter-vention group. A coin flip was used for ran-dom assignment to groups (i.e. heads �ANSWER strategy intervention and tails �non-intervention). An analysis of variance(ANOVA) was conducted that examined WJIIIBroad Reading scores for participants in theintervention (n � 11, M � 75, SD � 15.41)and non-intervention (n � 9, M � 73.33, SD �19.63) groups and revealed a non-significantdifference between the two groups, F(1, 19) �.045, p � .834, d � .09.

Materials

The lessons delineated in the Essay Test-Tak-ing Strategy manual (Hughes et al. 2005) wereadapted and included in the design and exe-cution of the ANSWER strategy intervention.The lessons in the Essay Test-Taking Strategymanual pertained to student handwritten es-say products. Adaptations were made to con-vert instructional materials to an electronicform so they could be used by the teacher andstudents via PC desktop computers. Teacherpresentation materials for respective lessonswere adapted to an electronic format in Mi-crosoft Word. During the ANSWER interven-tion electronic documents were shown via anoverhead projector connected to a PC com-puter. Student instructional materials werealso adapted and created in an electronic for-mat in Microsoft Word. For each lesson, in-structional materials were given to respectivestudents via USB memory sticks. Other adap-tations included in the ANSWER interventionlessons pertained to the use of the following:(a) individual student folders with theANSWER mnemonic on the front of thefolder, (b) highlighters, (c) USB memorysticks for each student that contained guidedpractice and independent practice passageswith a “TURN IN” folder for completed work,and (d) electronic and hard copy graphic or-ganizers for each lesson. The ANSWER mne-monic and steps are depicted in Table 1.

The original essay prompts created by


Therrien et al. (2009) and subsequently usedby Woods-Groves and colleagues in three pub-lished experimental ANSWER studies (i.e.,Woods-Groves et al., 2012, 2013, 2014) wereemployed in this investigation. The essayprompts incorporated in this study were coun-terbalanced and randomly assigned to partic-ipants for the pretest and posttest. Originallythe essay prompts were constructed to mirrorstatewide writing assessment probes and per-tained to the following topics: inventionsand heroes/heroines. The inventions promptstated “Inventions are all around us. Think of aninvention that has been especially helpful or harmfulto people. Write an essay that gives at least 3 reasonswhy the invention was helpful or harmful.” Thehero/heroine prompt stated “Your school news-paper is printing a series of articles about heroes andheroines. Write about someone who is a hero orheroine to you. That person may be someone youknow, someone you have read about, a celebrity, or ahistorical figure. Explain at least 3 reasons why youbelieve this person is someone to admire.” The fol-lowing maintenance prompt was designed tomirror material presented within lessons andthe Essay Test-Taking Strategy manual: “Youare going to have a week away from school. What is

your favorite thing you will do next week? List threereasons why this is your favorite thing to do.”

Design and Procedures

Design. An experimental design was em-ployed via a 2-level factor, randomly assignedintervention (treatment) or non-intervention(control) groups with pretest and posttest andmaintenance measures. As noted earlier, partic-ipants were stratified by their WJIII Broad Read-ing standard scores and a coin flip was used toassign individuals to intervention (i.e., heads)and non-intervention (i.e., tails) groups. Pretestsand posttests consisted of two counterbalancedessay prompts. A separate essay prompt was em-ployed for the maintenance measure.

Intervention. The Essay Test-Taking Strat-egy manual (Hughes et al., 2005) guidelinesdelineated the scope and sequence of thelessons incorporated into the ANSWER in-tervention. The manual instructional mate-rials were adapted for electronic use and theparticipants’ reading levels. The goal of theANSWER intervention sessions was to providestudents with instruction to support their con-struction of well-organized essay responses to

TABLE 1

ANSWER Strategy Lessons (adapted from Hughes et al., 2005)

L/S Lesson Activities

1/1 Students discussed their current essay response writing strategies, difficulties in writing, and theimportance of constructing good essay answers. Students were introduced to the essay-testtaking strategy with the mnemonic device ANSWER.

2/1 The instructor modeled the first two steps of ANSWER. Students underlined action words (e.g.,describe, list, summarize, or analyze) and to highlighted the requirements (e.g., how tomake a cake) for each essay prompt.

3/2 Students completed an independent practice worksheet about the first 2 ANSWER steps. Thenext two steps (i.e., Set up Outline and Work in Details) were introduced. Students read anessay prompt and completed the four ANSWER steps with guided practice, and anindependent practice worksheet.

4/2 Students read an essay prompt and the first four steps of ANSWER were reviewed with guidedpractice. The instructor modeled step five of ANSWER (i.e., Engineer the Answer) thatincluded writing an essay answer that contained the following: a) an introductory paragraphthat included a topic sentence, b) a paragraph for each main idea and related details, and c)a conclusion paragraph.

5/2 The first five steps of ANSWER were reviewed. The instructor led the students in guidedpractice using the five steps. The instructor modeled the final step (i.e., Review the Answer).Students reviewed their answers and conducted a spell check.

Note: L � Number of the Lesson conducted, S � how many sessions spent on each lesson.


essay test prompt questions. The studentswere instructed in examining respective essayprompt questions, planning and constructingan outline, and in constructing and reviewingtheir essay response.

The scope and sequence of the ANSWERinstruction consisted of five lessons that wereexecuted in eight sessions. Detailed lessonsteps are delineated in Table 1. In the firstANSWER lesson the rational for learning thestrategy was discussed with students and acommitment was obtained from the studentsto learn the strategy. The ANSWER subse-quent lessons pertained to teaching studentsto “Analyze” the essay prompt question and to“Notice the Requirements” in the electronicprompt by underlining and highlighting re-spective parts. Next, students were taught to“Set up an Outline” and “Work in the Details”by including main ideas and pertaining detailsin an electronic outline. Finally, students wereinstructed in constructing an electronic essayresponse by including outline componentsalong with an introductory paragraph, a para-graph for each main idea and respective details,and a conclusion paragraph. The last step wasfor students to “Review” their essay response bychecking the alignment with their outline andby using the spell checker function in MicrosoftWord Office. The graphic organizer employedin instruction contains each ANSWER step andis depicted in Figure 1.

For each lesson the instructor followed anadapted script from the Essay Test-Takingmanual. Each script included an advance or-ganizer and an explicit instruction format thatincluded the following components: (a) mod-eling, (b) thinking aloud methods coupledwith guided practice, (c) immediate correc-tive feedback, (d) independent practice,(e) students’ self-graphing their own perfor-mance. For each session students were givenUSB memory sticks with a guided practicepassage for whole class and instructor prac-tice, corrective feedback passage, and an inde-pendent passage. The USB memory sticks alsoeach had a TURN IN folder for completedwork.

Each student was given a folder for eachsession that had the ANSWER mnemonic onthe front of the folder and a step-by-step guidefor how to use the USB memory stick to accessand save files attached to the back of the

folder. On the inside of the student folder abar graph was attached so students couldgraph their graded independent work. Thestudents evaluated their own engagement,through a point booklet and earned participa-tion points each lesson based upon followingclass expectations (e.g., arrive to class on time,show respect to peers and teachers). A hardcopy graphic organizer was given to each stu-dent for each session’s lesson components.The ANSWER strategy six steps were pre-sented in a sequential format with each stepbeing taught to 80% mastery (i.e., determinedby independent work) before the next strat-egy step was introduced. Student goals wereindividualized with regard to essay construc-tion with some students writing multiple para-graphs while some students constructed a sin-gle paragraph.

Throughout the sessions two raters col-lected treatment integrity data. Each of thesessions’ lesson steps were delineated on re-spective lesson checklists. The raters checkedsteps as completed if the steps were observedby the raters during sessions.

Intervention group. As previously noted, stu-dents were randomly assigned to the interven-tion (treatment) group prior to the beginningof instruction. Each ANSWER session was 40minutes in duration. There were eight ses-sions that were conducted once a week for 8consecutive weeks. The total time for the du-ration of the intervention instruction was 6 hrs40 min. The intervention instruction occurredduring the students normally scheduled aca-demic activities and was conducted in a largegroup setting within a computer lab. Eachstudent was provided with a desktop PC andinstructional materials. The instructor had aprojector, and PC at the front of the classwhere instruction was conducted. The instruc-tor had an undergraduate degree in languagearts and was a certified general educationteacher who was employed by the postsecond-ary program. The first author met weekly withthe instructor to review each lesson and ma-terials prior to instruction.

Non-intervention group. Students who wererandomly assigned to the non-intervention(control) group participated in science in-struction at the same time the ANSWER inter-vention was being conducted. Science instruc-tion was conducted once a week for a period


of 40 min. for 8 consecutive weeks. All stu-dents in the intervention and non-interven-tion groups participated in their postsecond-ary delineated coursework. Common coursesincluded instruction in finance, current is-sues, and vocational internships.

Dependent variables. The intervention andnon-intervention groups’ pretest and posttestresponses to the essay prompts were assessedvia the strategy scoring rubric. This rubric had

been previously used as a dependent measurein the four published experimental ANSWERstudies (Therrien et al., 2009; Woods-Groveset al., 2012, 2013, 2014) and is depicted inFigure 1. The strategy scoring rubric is a toolto assign scores that could range from 0 to 6for a possible total score. The rubric is alignedto match to specific ANSWER strategy steps.Steps 1 through 4 pertain to (strategy specificsteps) analyzing the essay prompt and to set-

Figure 1. ANSWER MNEMONIC (adapted from Hughes et al. 2005).


ting up an outline and can be scored a rangeof 0 to 4 points. Steps 5 through 6 are delin-eated in the rubric (general component steps)and pertain to the construction of the essayresponse and reviewing the essay responsewith possible scores ranging from 0 to 2points. See Figure 2 for the strategy scoring

rubric. Two graduate student raters employedthe strategy specific rubric to evaluate thepretest and posttest essays and maintenanceessays that were constructed by the students.The mean of the raters’ responses were cal-culated for the strategy specific steps (1through 4), the general component steps (5

Figure 2. Strategy Scoring Rubric (adapted from Therrien et al. [2009], Woods-Groves et al. [2012],Woods-Groves et al. [2013], Woods-Groves et al. [2014]).


through 6), and the overall total score (steps1 through 6).

The second dependent measure was usedby the raters to provide a more stringent eval-uation of the students’ constructed essays.This measure was a holistic analytic scoringrubric developed by the Oregon Departmentof Education and denoted as an “Official Scor-ing Guide” (Oregon Department of Educa-tion, 2004–2005). This measure had beenused in two previous experimental investiga-tions of the ANSWER strategy (i.e., Therrienet al., 2009; Woods-Groves et. al., 2014) andprovided an evaluative criteria similar to oftenused state writing assessments (Isaacson,1996). Six domains were evaluated with thisrubric which included: ideas and content, or-ganization, voice, word choice, sentence flu-ency, and conventions. Each domain couldreceive a score that ranged from 0 to 6 with 6indicating the highest quality.

Data collection. Following the random as-signment of students to the intervention andnon-intervention groups, all students were ad-ministered a pretest with counterbalancedprompts (prompt 1 and prompt 2). A posttestwas administered one week following the con-clusion of ANSWER instruction. The posttestwas given to all students. A maintenanceprompt was administered to all students 2weeks following the end of ANSWER strategyinstruction. Two graduate students enrolledin the doctoral program in special educationused the strategy rubric and the analytic ru-bric to evaluate de-identified pre- and posttestessays, and maintenance essays. The raters didnot have knowledge of the ANSWER strategyinstruction or if the essays were from studentsin the intervention group or non-interventiongroup. The first author trained the studentraters in how to use the respective rubrics toevaluate the students’ completed essays. Theraters practiced using the rubrics to evaluateessays and met to discuss their ratings witheach other while they were evaluating the es-say responses.

Data Analysis

The software G power 3 (Faul, Erdfelder,Lang, & Buchner, 2007) was used to conducta power analysis. Previous experimental stud-ies for the ANSWER strategy reported Co-

hen’s d effect sizes that ranged from d � .95 tod �15.85 (Therrien et al., 2009; Woods-Groveset al., 2012, 2013, 2014). The power analysiswas conducted with a large effect size of .80,an alpha of .05, and .80 for power. The resultsindicated that a total sample size of 15 wouldbe adequate (N � 14.6429 – � 15). Additionalanalyses were conducted via IBM SPSS 23(2016). A series of analysis of variance (ANO-VAs) were used to compare pretest strategyand analytic scoring rubric results from inter-vention and non-intervention groups. Ananalysis of covariance (ANCOVAs) with pre-tests as the covariates strategies were used toexamine the strategy and analytic scoring ru-brics posttest results for the intervention andnon-intervention groups. The strength of sta-tistically significant findings (effect sizes) werecalculated and then evaluated via Cohen(1988) classification standards (i.e., .2 �small, .5 � medium, and .8 � large).

Results

Treatment Integrity and Inter-Rater Reliability

For each of the eight ANSWER sessions treat-ment integrity was collected via two raterscompleting respective lesson related check-lists. Each checklist contained the content andsteps to be included in each session. For sevenof the eight lessons two raters agreed 100%with regard to content and steps completed ineach session. For one session treatment integ-rity was collected by one rater due to schedul-ing conflicts. Treatment integrity for this ses-sion indicated 100% compliance.

Interrater agreement for the two raters’ re-sults from the strategy scoring rubric and theanalytic scoring rubric was calculated via bi-variate correlations. Correlations were exam-ined for the two raters’ scores via the respec-tive rubrics (i.e. strategy scoring rubric andthe analytic rubric) for the pretests, posttests,and maintenance. Correlations for the strat-egy scoring rubric results across essay promptsbetween raters ranged from r �.89 to 1.00,mdn � .99. For the analytic rubric results forpretest and posttest correlations between thetwo raters ranged from r �. 91 to .98, mdn �.95. For the analytic rubric raters’ results forthe maintenance essay responses correlationsranged from .73 to .87, mdn � .79.


Posttest Measures

Strategy-specific rubric. The differences be-tween the treatment and control groups’ post-test essays were examined using a series ofANCOVAs. The results for the overall strategyrubric between the treatment and controlgroups were statistically significant with a largeeffect size for the treatment group (p � .008,d � 1.33). The strategy-rubric componentswere broken down into two parts to furtherexamine what might account for the signifi-cant difference. The strategy-use componentssteps 1–4) for the treatment group were sig-nificantly higher, with a large effect size (p �.012, d � 1.31). For the general components(steps 5–6), the treatment group significantlyoutperformed the control group, with a largeeffect size (p � .021, d � .90). Table 2 detailsthe means, standard deviations, p values, ef-fect sizes, ANOVA, and ANCOVA results forthe strategy-scoring rubric for the pre- andposttests.

Analytical-scoring rubric. The participants’posttest essays responses were evaluated usinga 6-point scale that ranged from 1 for thelowest point to 6 for the highest. The rubricincludes six analytic areas: ideas/content, or-ganization, voice, word choice, sentence flu-ency, and conventions. The ANCOVA posttestresults, with pretests as the covariate, for theintervention and non-intervention group re-sponses were significant in favor of the inter-vention group for “ideas/content” and “wordchoice” with large effect sizes of p � .039, d �.71 and p � .011, d � .86, respectively. How-ever, non-significant results were revealed forthe rest of the analytic rubric areas. Table 2details the mean values, standard deviations, pvalues, effect sizes, ANOVA, and ANCOVA re-sults for the analytic rubric for the pre- andposttests.

Maintenance. A maintenance essay promptwas administered 2 weeks after the ANSWER strat-egy intervention was completed. The AN-SWER strategy was not reviewed. An electronicWord document that contained the essayprompt was loaded on the PC desktop foreach student in a large group format. Thegraduate students’ mean strategy scoring ru-bric scores for the intervention group andnon-intervention group were examined viaANCOVAs with the pretests as the covariate.

The overall rubric total revealed that the in-tervention group significantly outperformedthe non-intervention group, p � .001, d �.2.26. For strategy use and general compo-nents the intervention group, significantlyoutperformed the non-intervention groupp � .001, d � .2.32. and, p � .004, d � 1.40,respectively. The intervention group signifi-cantly outperformed the non-interventiongroup in the analytic rubric rating for wordchoice, p � .036, d � .68. A comparison ofintervention and non-intervention groups re-vealed non-significant results for the analyticrubric ratings for ideas/content, organization,voice, sentence fluency, and conventions.Table 3 depicts ANCOVAs, Mean, SD, effectsizes, and p-values for maintenance compari-sons.

Discussion

This study investigated the efficacy of the AN-SWER strategy to improve electronic essaywriting skills of college students with IDD.Students who were taught the ANSWER strat-egy once a week for 8 weeks (i.e., a total of 6hrs 40 min.) used the strategy steps as theyconstructed their essay test responses. Stu-dents in the treatment group improved theiressay writing skills in the areas of overall essayquality, the use of ideas and content, and wordchoice when compared students in the con-trol/non-intervention group. In the 2 weeksfollowing the end of ANSWER instruction, stu-dents in the treatment group maintained theirskills in overall essay quality that includedstrategy use, quality construction of the essayresponses, and word choice when comparedto the control group. The results from theproximal (i.e., Strategy Rubric) and distal(i.e., Analytic Rubric) were similar to thosefound in Woods-Groves et al. (2014) wheresignificant effects were found for the treat-ment group for both metrics for college stu-dents with IDD where students who weretaught the answer strategy constructed betterquality essay responses than students whowere not taught the strategy.

The four previous experimental ANSWERstudies instructed students in constructing handwritten essay outlines and essay responses. InTherrien et al. (2009) middle schoolers withLD who were taught the ANSWER strategy sig-


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nificantly improved their essay responses.Woods-Groves et al. (2012) revealed that col-lege students with IDD learned and appliedthe ANSWER strategy. Woods-Groves and col-leagues conducted two additional studies thatsupported the use of the ANSWER strategy toimprove the quality and organization of col-lege students with IDD’s essay responses(Woods-Groves et al., 2013, 2014). This exper-imental study examined the use of theANSWER strategy in an electronic formatwhere students applied the steps of the strat-egy when presented with an essay question ina word document via a desktop computer.The students analyzed the action words andnoticed the requirements by underlining andhighlighting the essay prompt question, con-structed an electronic outline, constructed anessay response, and revised their response allthrough their word document and desktopcomputer.

Limitations and Future Research

There were several limitations to this study.The first limitation pertained to the fact thatwhile students in the treatment group signifi-cantly outperformed students in the controlgroup on the distal analytic rubric in the areaof idea/content and word choice the 2-weekmaintenance results revealed that students’essays were not scored as significant in thearea of idea/content. This could indicate anextension in instructional time may be neededto ensure students have overlearned the strat-egy and that students have a chance to applythe strategy over a longer period of time. The2-week maintenance time was a relatively shortperiod of time between the end of interven-tion and the maintenance phase. This oc-curred due to the length of the academic year.A second limitation is related to schedulingand the length of the academic year and per-tains to the lack of a generalization phase.Unfortunately, we were not able to ascertain ifstudents in this study would have generalizedtheir essay writing skills. Future studies shouldinclude a longer maintenance phase periodand a generalization phase. The third limita-tion pertains to the need to examine theANSWER strategy’s use in different contextswhere expository writing tasks are assignedsuch as in-person college classes in differentT

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subject areas, in one-to-one tutorial services,and through online college coursework as-signments.


MacArthur, Graham, and Fitzgerald (2016)noted that “writing is critical to the advance-ment of knowledge in academic, technical,and business fields. . . .” (p. 1). Students withand without disabilities who experience diffi-culties in written expression throughout theirsecondary settings oftentimes will enter col-lege or the workforce ill prepared to completecore aspects of their educational program orjob. It is essential that teachers use evidence-based strategies in writing instruction. Asnoted earlier, 75% of writing tasks are expos-itory for 12th graders, with that proportionincreasing in college (Graham & Perin, 2007).With the majority of secondary students ineighth and 12th grades who were assessed viathe NAEP falling in the non-proficient rangein the area of writing there is a need to employeffective writing practices in the classroom.The writing process is iterative in nature andinvolves metacognition, motivation, and canbe context dependent (Hayes, 1996; 2012;Hayes & Flower, 1980). Individuals write formany purposes and within many contexts.Components of the ANSWER strategy are un-dergirded by previous empirical work re-viewed by Graham et al. (2016) in the area ofwriting (i.e. use of explicit instruction, self-assessment through goal setting and graph-ing, use of feedback during guided practice,using word processing in writing). Within sec-ondary and college classrooms students arecalled upon to construct text in an electronicformat and are oftentimes assessed via com-puter-based platforms where they constructquality of essay responses.

When differentiating instruction it is bene-ficial for educators to identify and use strate-gies that can be effective for all learners. Theefficacy of the ANSWER strategy has been sup-ported through five experimental group stud-ies that included middle schoolers with LD orcollege students with IDD. These studies sup-port the use of the ANSWER strategy as aninstructional method that has been effectivein improving the quality of essay test re-sponses.

This current investigation extended previ-ous work pertaining to the ANSWER strategyand handwritten essay responses to embed-ding the strategy in an electronic-based for-mat. It is important to support 21st centurylearners with and without disabilities whostruggle in written expression with instructionthat includes pre-planning, essay construc-tion, and revision of text within a word pro-cessing or equivalent electronic format. Forcollege students with IDD who struggle in writ-ing and who are entering IHEs it is imperativeto provide effective instruction in a timelymanner. We encourage future exploration ofthe efficacy of ANSWER strategy in improvingessay writing for secondary and college-agestudents with disabilities who experience dif-ficulties in writing.

References

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Pennington, R. C., & Delano, M. E. (2012). Writinginstruction for students with autism spectrum dis-orders: A review of literature. Focus on Autism andOther Developmental Disabilities 27, 158–167.

Therrien, W. J., Hughes, C. A., Kapelski, C., &Mokhtari, K. (2009). Effectiveness of a test-takingstrategy on achievement in essay tests for studentswith learning disabilities. Journal of Learning Disabil-ities, 42, 14–23. doi: 10.1177/0022219408326218

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Woods-Groves, S., Therrien, W. J., Hua, Y., & Hen-drickson, J. M. (2013). Essay writing strategy forstudents enrolled in a post-secondary program forindividuals with developmental disabilities. Reme-dial and Special Education, 34, 131–141.

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Received: 25 May 2017Initial Acceptance: 1 August 2017Final Acceptance: 29 September 2017


http://www.realityeducation.com/SixTraitWritingRubric_000.htm

http://www.realityeducation.com/SixTraitWritingRubric_000.htm

http://www.thinkcollege.net

http://whatworks.ed.gov

http://whatworks.ed.gov

Teaching Students with Autism and Intellectual Disability toSolve Algebraic Word Problems

Jenny R. Root and Bonnie HenningFlorida State University

Erica BoccuminiWalker Elementary School

Abstract: This study used modified schema-based instruction (MSBI) to teach mathematical word problemsolving to three students with ASD in fifth and sixth grade. Following explicit strategy instruction, theparticipants learned to use an 8-step task analysis and a graphic organizer to solve and discriminate betweenmath word problems requiring algebraic reasoning. A multiple probe across participants design was used toestablish a functional relation between MSBI and word problem solving, with a non-parametric measure(Tau-U) confirming visual analysis of a large effect (.87). Results indicated students increased the number ofsteps of the task analysis solved independently correct, total problems solved, and discrimination of problem type.Implications for practice and future research are discussed.

State standards like the Common Core StateStandards Mathematics (CCSSM) help educa-tors set targets for mathematical learningexperiences. One domain of mathematicsemphasized throughout grade levels in theCCSSM is algebra (Common Core State Stan-dards Initiative, 2015). Algebraic reasoningserves as a bridge for the “cognitive gap” stu-dents often experience between arithmeticand algebra (Herscovics & Linchevski, 1994;Witzel, 2016). The foundations of algebraicreasoning are laid in early elementary gradesas children learn to form generalizations aboutnumbers, notice patterns, and reason aboutequivalence (Van de Walle, Karp, & Bay-Wil-liams, 2016). For example, identifying whethertwo groups of objects have an equal quantity ofitems is an essential prerequisite skill to under-standing properties and solving equations. Thisskill is necessary when deciding whether a givenquantity is “enough” or “not enough” for a giventask and what further steps might need to betaken. Reasoning is a defining feature of math-ematics and is essential for conceptual under-standing (NCTM, 2000).

There is a relationship between algebraicreasoning and word problem solving. Powell

and Fuchs (2014) found second grade stu-dents who struggled with word problem solv-ing performed lower on algebraic reasoningtasks than students who had difficulty withcalculation alone. Problem solving experi-ences in school settings are typically struc-tured in the format of story problems. In thecontext of word problem solving, stories pres-ent situations requiring a mathematical solu-tion (Stein, Kinder, Silbert, & Carnine, 2006).Learning to solve word problems is the basisfor learning to solve real-world problems (Vande Walle et al., 2016). Consideration of the“school effects” of mathematics highlights theneed for high quality instruction, as school islikely to be the only context in which studentsreceive instruction in mathematics, unlike lit-eracy or reading (Van de Walle et al., 2016).

Students with mathematical difficulties, in-cluding those with autism spectrum disorder(ASD), need explicit instruction on learningstrategies in order to make progress in alge-braic problem solving. Jitendra and colleagues(2015) established learning strategies thatprime the problem structure as an evidence-based practice for teaching mathematicalproblem solving to students with mathemati-cal difficulty. Two learning strategies thatprime problem structure are schema-broaden-ing instruction and schema-based instruction.Both schema-broadening and schema-basedinstruction use explicit instruction to teach

Correspondence concerning this article shouldbe addressed to Jenny R. Root, School of TeacherEducation, Florida State University, 1114 W. Call St.,Tallahassee, FL 32306. E-mail: [email protected]


Word Problem Solving / 325


students to conceptually understand the struc-ture of problems and traditionally use mne-monics, such as RUNS (Read the problem,Underline key information, Name problemtype, Solve) or FOPS (Find the problem type,Organize the information using diagram, Planto solve the problem, Solve the problem) toprocedurally solve the problem. Fuchs et al.(2014) used a schema-broadening approachthat explicitly taught students the underlyingstructure of three additive problem types(e.g., group, change, and compare) usingreal-life scenarios. Students were taught a sys-tematic process for using an equation to ana-lyze components of word problems and rep-resent them using mathematical terms andsymbols. Fuchs et al. (2014) found explicitword problem solving instruction that primedthe problem structure had a greater effect onpre-algebraic knowledge for elementary stu-dents than calculation based instruction. Bothschema-broadening and schema-based in-struction involve instructional strategies thataddress the barriers to success in mathematicsfaced by students with ASD (Rockwell, Griffin,& Jones, 2011).

The unique learning characteristics of indi-viduals with ASD related to working memory,executive functioning, and language contrib-ute to their difficulties with word problemsolving. According to Kintsch and colleagues(Cummins, Kintsch, Reusser, & Weimer, 1988;Kintsch & Greeno, 1985; Nathan, Kintsch, &Young, 1992), word problem solving is an in-teraction between problem-solving strategiesand language comprehension processes. Forstudents with ASD, deficits in working mem-ory, executive functioning, and language cre-ate barriers to both conceptual understandingof what is happening in word problems as wellas creating and carrying out a procedural planfor solving the problem. In addition, manyindividuals with ASD have below averagemathematics word problem solving and calcu-lation skills (Wei, Christiano, Jennnifer, Wag-ner, & Spiker, 2014) and nearly 25% of stu-dents with ASD have a mathematics learningdisability (Mayes & Calhoun, 2006). Comor-bidity of ASD and an intellectual disability(ASD/ID) likely negatively effects problemsolving abilities, as these students have deficitsin the necessary component skills of word de-coding, mathematical vocabulary, computa-

tion, and everyday mathematical knowledge(Bae, Chiang, & Hickson, 2015).

Recent research has found modified sche-ma-based instruction (MSBI) effective in over-coming the barriers to problem solving facedby individuals with ASD/ID (Spooner, Saun-ders, Root, & Brosh, 2017). MSBI teaches con-ceptual and procedural knowledge by com-bining traditional schema-based instructionwith established evidence-based practices forteaching mathematics to students with severedisabilities, including systematic promptingand use of a task analysis (Browder, Spooner,Ahlgrim-Delzell, Harris, & Wakeman, 2008;Spooner, Root, Saunders, & Browder, 2018).While semantic diagrams are an essential com-ponent of schema-based instruction, MSBIprovides students with graphic organizers as avisual support, which is an evidence-basedpractice for students with ASD (Wong et al.,2014) and is recommended by Barnett andCleary (2016) as a strategy for increasing thesuccess of students with ASD with algebraicproblem solving.

Through a series of single-case designs,Browder and colleagues evaluated the effectsof MSBI on the mathematical problem solvingof 10 elementary and 13 middle school stu-dents with developmental disabilities, includ-ing 14 students with moderate ID and ninewith ASD/ID (Browder et al., 2018; Root,Browder, Saunders, & Lo, 2017; Root &Browder, 2017; Root, Saunders, Spooner, &Brosh, 2017; Saunders, 2014; Saunders,Spooner, & Ley Davis, 2018). Results of sup-porting studies have found MSBI to be effec-tive in teaching one-step additive problems(group, change, and compare), including onestudy that taught algebraic word problems(Root & Browder, 2017).

Root and Browder (2017) taught three mid-dle school students with ASD/ID to use anelectronic task analysis to solve algebraic wordproblems with the missing information inboth the medial (i.e., 3 � x � 5) and final(i.e., 3 � 2 � x) positions. Following guide-lines of Spooner, Saunders, et al. (2017) wordproblems depicted quantities less than 10and participants used manipulatives on thegraphic organizer to solve the problems. Afunctional relation was found between MSBIand problem solving, but participants had lim-ited generalization of problems with missing


information in the medial position when vi-sual supports were faded. This may have beendue to limited conceptual understanding ofthe difference between the two problem types,as they were taught simultaneously with noexplicit discrimination training.

While there is some empirical support forthe use of MSBI to teach problem solving tostudents with ASD/ID, existing studies haveseveral limitations that place boundaries ongeneralization of problem solving. First, allstudies taught problem solving with quantitiesless than 10 in accordance with the early nu-meracy skills of participants. As such, manipu-latives were used as concrete representationsto procedurally solve the problem. Althoughmanipulatives are an evidence-based practicefor students with moderate/severe disability(Spooner, Root, et al., 2018), their use is notfeasible with larger quantities. There are manyreal-world applications of mathematical prob-lem solving that require computation withquantities above 10. Given its emerging recordof success for teaching problem solving tostudents with ASD/ID, further research is war-ranted on the feasibility of MSBI to addressalgebraic reasoning and problem solving withquantities above ten and discrimination ofproblem types. Therefore the purpose of thisstudy was to evaluate the effects of MSBI onsolving problems that require algebraic rea-soning by students with ASD by addressing thefollowing research questions:

1. What is the effect of modified schema-based instruction on algebraic wordproblem solving of students with ASD?

2. What is the effect of modified schema-based instruction on discrimination ofproblem type by students with ASD?

Method

Participants

Approval from Institutional Review Board wasreceived prior to recruitment. Students wererecruited using teacher nomination and wereeligible to participate based on the followinginclusion criteria: (a) an educational or med-ical diagnosis of ASD and (b) satisfactory per-formance on a pre-screening measure. Theprescreening tool evaluated student’s ability

to (a) identify double digit numbers, (b) addand subtract single and double digit numbers,(c) write numbers using a pencil, and (d)solve one-step word problems. Performanceon the prescreening measure was consideredsatisfactory if participants were able to com-plete items a, b, and c with 100% accuracy andno more than 20% accuracy on item (d).Three students with ASD participated in thestudy. Prior to the beginning of the study, allparticipants were administered the mathemat-ics battery from the Woodcock Johnson Testsof Achievement, 3rd edition (WJ-III; Wood-cock, McGrew, & Mather, 2001) by the secondauthor.

Ricky was a 10-year-old Caucasian male witha combined diagnosis of ASD level 1, ADHD,and Obsessive Compulsive Disorder from aphysician. Standardized assessment informa-tion regarding Ricky’s cognitive or adaptivefunctioning was not available. He enjoyed talk-ing with peers and familiar adults and wasquick to advocate for himself, such as by say-ing “I can do that” or “No thanks”. Ricky hadan overall mathematical standard score of66 (1st percentile) on the WJ-III. He hadstrengths in calculation with a standardscore of 83 (13th percentile) and weaknessesin applied problems with a standard score of63 (1st percentile).

Kelly was an 11-year-old Caucasian femalewith a diagnosis of ASD level 2, language im-pairment, and mild intellectual disability froma physician. Standardized assessment informa-tion regarding Kelly’s cognitive or adaptivefunctioning was not available. Kelly enjoyedbeing with peers and adults would greet peo-ple who entered the room. Kelly often talkedusing delayed echolalia phrases such as “Didyou get the mail today?” She could respond toquestions and directions asked by teachers.Kelly had an overall mathematical standardscore of 60 (�1st percentile) on the WJ-III.She had strengths in calculation with subteststandard scores of 81 (10th percentile) andweaknesses in applied problems with a stan-dard score of 55 (�1st percentile).

Marc was a 12-year-old Caucasian male witha combined diagnosis of ASD level 3, ADHD,and a mild intellectual disability from a physi-cian. Marc’s most recent comprehensive eval-uations indicated an IQ score of 58 on theStanford-Binet Intelligence Scales, 5th edition


(Roid, 2003). Marc interacted with peers andadults within the classroom through greetingsand asking questions. He could respond toquestions and requests asked by teachers. Attimes, Marc’s speech was repetitive if his com-munication partner did not follow the pattern(e.g. Marc would keep saying “How are you?”if you the other person did not say “And howare you?”) On the WJ-III, Marc had an overallmathematical standard score of 25 (�1st per-centile). Although his performance was in�1% percentile, Marc had a higher standardscore in the area of applied problems (46)compared to calculation (20).

Setting

This study took place at a private school forstudents with ASD located in the southeasternUnited States. Participants were in the samemulti-grade classroom. They participated indaily instruction from a female non-certifiedteacher who was trained as a registered behav-ior technician (RBT) and was working to com-plete requirements to become a Board Certi-fied assistant Behavior Analyst (BCaBA).Several teacher assistants who were also RBTswere present in the classroom throughout theday. Participants were engaged in approxi-mately 60 min of daily mathematics instruc-tion from the classroom teacher using freeonline materials from Engage NY (New YorkState Education Department, 2017; www.engageNY.org) aligned to 3rd to 5th gradeCommon Core State Standards.

Intervention sessions were conducted oneon one between the interventionist and par-ticipant approximately four days per week in asmall quiet classroom free from visual and audi-tory distractions. Each session lasted approxi-mately 10 min. The first and second authorswere both interventionists. The first author has adoctorate in special education, is a Board Certi-fied Behavior Analyst (BCBA), and former spe-cial education teacher. The second author was adoctoral student in special education and a for-mer special education teacher.

Materials

Student materials included an 8-step task anal-ysis (TA), worksheets and pencils with erasers.Each page of the worksheet had one word

problem (either missing-whole or missing-part), a graphic organizer, and a structure forequations. The word problems were all of the“group” problem type, which consist of twosmall groups that can be combined to createone large group (Carpenter & Moser, 1984).These problems reflect a part-part-whole rela-tionship. Group problems can be written ei-ther as addition problems, where the knownvariables are the small groups, or as a subtrac-tion problem where the known variables arethe large group and one of the small groups.For example, the problem “Jack had comicbooks on his iPad. Jack has 15 superherocomic books. Jack has 10 anime comic books.How many comic books does he have on hisiPad altogether?” would be solved by writing theequation 15 � 10 � X, with the “missing” infor-mation located in the final position. This prob-lem type was characterized as ‘missing whole’(MW) for the purpose of the study. The sameproblem could be rearranged to have themissing information in the initial or medialposition; “Jack had comic books on his iPad.Jack had 15 superhero comic books and someanime comic books. If he has 25 total comicbooks, how many are anime?” This problemwould be solved by writing the equation 15 �X � 25. It could also be solved as 25 � 5 � X.These problems were characterized as ‘missingpart’(MP) for the purpose of this study. Wordproblems were written following guidelines pro-vided by Spooner, Saunders, et al. (2017) andused quantities above 10.

In addition to the word problem, each pageof the worksheet also had a pre-drawn graphicorganizer (see Figure 1). This graphic orga-nizer had two small circles above one largercircle. At the bottom of the worksheet was anadditional visual support to help participantsline up vertical equations which consisted ofsix empty squares aligned in three rows of twowith a solid bar for the equal sign and anempty circle for the operation sign to the leftof the middle row of boxes. An 8-step TA wasprinted on a separate piece of 8.5� � 11�paper. Participants used a new TA for eachproblem. Each step of the TA had a picture tosupport understanding and a space to checkoff each step as it was completed to promoteself-management. See Table 1 for the steps ofthe TA and expected student responses.


http://www.engageNY.org

http://www.engageNY.org

Design and Measurement

A multiple probe across participants designwas used to demonstrate a functional relationbetween the mathematics intervention andthe primary dependent variable (Horner &Baer, 1978). All three participants enteredbaseline together and were continuouslyprobed. The implementation of the designadhered to the criteria established by theWhat Works Clearinghouse (WWC; Kratoch-

will et al., 2013). There were three experimen-tal conditions: baseline, intervention, andprobe. The intervention consisted of threephases: missing whole (MW), missing part(MP), and discrimination. A three-sessionprobe was conducted between each interven-tion phase. After the first participant (Ricky)showed a clear accelerating trend or improvedlevel of a minimum of three data points in theprimary dependent variable during missing-

Figure 1. Graphic organizer and structured equation.


whole intervention, the second participant(Kelly) entered intervention. Following mas-tery in each intervention phase, participantswere probed for three sessions to measuremaintenance of treatment effects and gener-alization of effects to the other problem type.

Dependent variable. The primary depen-dent variable was mathematical problem solv-ing, measured by the number of steps of the8-step TA competed independently correct.Although the TA consisted of eight steps, thefinal step (solve and write answer) was worthtwo points, one for each separate behavior(see Table 1). Participants solved two prob-lems of the targeted problem type in eachsession, for a total of 18 available points foreach problem type in each session. The sec-ond dependent variable, discrimination ofproblem type, was only measured in baselineand probe trials when no prompting and feed-back were provided. Discrimination of prob-lem type was defined as participants selectingthe appropriate operation (addition for miss-ing-whole, subtraction for missing-part). Par-ticipants had the opportunity to solve twoproblems of each problem type during base-line and probe sessions for a total of fouropportunities for discrimination in each ses-sion.

Interobserver agreement and procedural fidelity.To ensure reliability and fidelity, interob-server agreement (IOA) and procedural fidel-

ity data were collected across all experimentalconditions. Both in vivo and permanent prod-uct (video) observations were used. The sec-ond observer (third author) was trained tofidelity and 100% agreement across both mea-sures was reached via both role playing andobservation of participant videos.

The second observer collected IOA duringfor 60% of baseline sessions for Ricky (threeout of five sessions), 42% of baseline sessionsfor Kelly (three out of seven sessions), and37% of baseline sessions for Marc (three outof eight sessions). The agreement was 100%for all three participants during baseline. Thesecond observer collected IOA during at least20% of all probe and intervention sessions,including at least once in each phase/probe.IOA was collected for 36% of intervention andprobe sessions for Ricky (seven out of 19 ses-sions), 50% of intervention sessions for Kelly(nine out of 18 sessions), and 35% of inter-vention sessions for Marc (12 out of 34 ses-sions). Mean agreement for intervention andprobe sessions was 98% (range 90–100) forRicky, 97% for Kelly (range 90–100), and 97%(range 80–100) for Marc.

The second observer used a procedural fi-delity checklist to document the degree towhich the intervention was implemented con-sistently as designed. For each step of the taskanalysis, the second observer evaluated thefollowing actions of the interventionist:

TABLE 1

Expected Student Responses for Each Step of Self-Instruction Sheet

Step Expected Student Response

1. Read the problem Read problem or ask for problem to be read2. Circle the groups Circle the whole (“big group”) and parts (“small groups”) in problem3. Circle the numbers Circle numbers in word problem4. Label graphic organizer Write numbers and labels of groups on graphic organizer; put an “x”

next to the unknown quantity5. Sign and say the rule State rule/use handmotions for problem type: “small group plus

small group equals big group” for missing whole or “big groupminus small group equals small group” for missing part

6. Fill in equation Write numbers from graphic organizer in equation; two small groupsfor missing whole, big group on top and small group on bottomfor missing part

7. � or � Write addition (missing whole) or subtraction (missing part) symbolin the circle in equation

8. Solve and write answer Add or subtract and (1) write numerical answer and (2) label ofunknown quantity


(a) reinforcement, (b) adherence to prompt-ing hierarchy, and (c) model for error correc-tion. The second observer collected proce-dural fidelity data during all sessions whenIOA was collected. Procedural fidelity wascalculated by dividing the number of stepscorrectly implemented by the total numberof procedural steps and multiplying the quo-tient by 100 (Billingsley, White, & Munson,1980). The mean procedural fidelity in in-tervention was 98% for Ricky (range 90 –100), 98% (range 90 –100) for Kelly, and97% (range 80 –100) for Marc.

Procedures

Baseline and probes. Participants were pro-vided worksheets with four problems (two ofeach type), a pencil with an eraser, and theTA. The interventionist asked the participantsto “Show me how to solve these word prob-lems”. Word problems were read aloud if re-quested. Participants were given intermittentpraise for completing work and staying on task(e.g., “You are working so hard”), but no spe-cific feedback on correct or incorrect re-sponses was provided.

Intervention. Participants were taught tosolve the problems using MSBI. Key compo-nents of MSBI include: (a) creating access tothe problem through read-alouds and accessi-ble word problems, (b) promoting conceptualunderstanding of problem by providinggraphic organizers, and (c) explicitly teachingsteps of a task analysis with systematic prompt-ing as needed (Spooner, Saunders, et al.,2017). At the beginning of each phase (e.g.,MW, MP, and discrimination), the interven-tionist modeled how to use the TA to solve thetargeted problem type for two days. Duringmodeling sessions, the interventionist in-structed participants on how to use the TA tosolve the targeted problem, allowing them animmediate opportunity to complete each step.No data was collected during these sessions, asparticipants did not have an opportunity tomake an incorrect response. See Table 1 foreach step of the TA and expected studentresponse.

The “lead” phase began on the third sessionin each phase, and a system of least promptswas used if the participant failed to make aresponse within five seconds. The prompting

hierarchy included a verbal prompt (e.g., re-stating the step of the TA), specific verbalprompt (e.g., providing specific directions onhow to complete the step) and a model-retest(e.g., showing participant how to completethe step and re-presenting step). If the partic-ipant made an error, such as choosing thewrong operation, the interventionist went di-rectly to a model prompt followed by a retestwhere the participant repeated the correctbehavior (i.e., writing the correct operation inthe equation).

Once participants had reached the masterycriteria of 16/18 steps completed indepen-dently correct across two problems of targetedproblem type, a series of three probes (e.g.,“test” phase) were conducted to assess main-tenance of problem solving without prompt-ing or feedback, as well as the degree of gen-eralization and discrimination of problemsolving across problem types. Following thethree probe sessions, the interventionist be-gan the explicit instruction sequence (i.e.,modeling) with the next intervention phase.

In initial intervention sessions, behaviorspecific praise was provided after each stepregardless of whether it was an independentor prompted correct response. Behavior spe-cific praise was faded to only unpromptedcorrect and then to completion of the prob-lem as participants demonstrated mastery ofproblem solving. Two problems of the tar-geted problem type were presented duringeach intervention session. During the finalintervention phase when participants weretaught how to discriminate between MW andMP, a total of four problems were presentedin each intervention session (two of eachtype). Data were taken on the number of stepsof the TA the participant completed indepen-dently correct.

Results

Mathematical Problem Solving

Figure 2 shows the effect of MSBI on mathe-matical problem solving. The graph shows thenumber of steps of an 8-step TA performedindependently correct across two problems ofeach problem type, with step 8 of the TAworth two points, one for each behavior (i.e.,solve and write answer) during baseline, inter-


vention, and probe sessions. Not pictured onthe graph are the two training sessions at thebeginning of each intervention phase, as stu-dents were not given the opportunity to makean independent response.

During baseline all participants had a stablepattern of responding. Immediately upon re-ceiving instruction on solving missing-whole

problems, all three participants showed achange in level and increasing trend, with nooverlapping data with baseline performance.Data from the first probe for each participantshow maintenance of treatment effects formissing-whole problems and some generaliza-tion to the missing-part problems, but theywere unable to discriminate between problem

Figure 2. Graph of number of steps of task analysis completed independently correctly. Note: Open circlesindicate missing part (MP) problems; closed circles indicate missing whole (MW) problems. BL �baseline, MW � missing whole, P1 � probe 1, MP � missing part, P2 � probe 2, Disc �Discrimination between missing whole and missing part, P3 � probe 3. � indicates beginning toprovide a highlighter.


types (see Figure 3). Immediately upon receiv-ing instruction on solving missing-part prob-lems, all three participants showed a changein level and increasing trend, with no overlap-ping data with baseline or probe perfor-

mance. Data from the second probe for eachparticipant shows maintenance of treatmenteffects for missing-part problems, but a de-crease in missing whole problems, and an in-ability to discriminate between problem types.

Figure 3. Discriminations of problem type during baseline and probe sessions.


Immediately upon receiving discriminationtraining, participants increased independencein both problem types. Data from the finalprobe for each participant shows maintenanceof treatment effects and discrimination ofproblem types for all participants. An effectsize measure was calculated to confirm visualanalysis (Tau-U), using an online calculator(Vannest, Parker, Gonen, & Adiguzel, 2016),resulting in an overall effect size of .87. Table2 displays the mean and range independentresponses to the task analysis for each prob-lem-type by phase across participants. Figure 3displays a graph of discrimination of problemtype by participants across phases.

Ricky had a stable baseline for both missing-whole (average 6.2 steps) and missing-part(average 0 steps) across five sessions. After twosessions of modeling, he increased indepen-dent responding and was able to reach mas-tery in three sessions. During the first probehe maintained a mastery of MW by perform-ing all 18 steps across the two problems inde-pendently correct and generalized some re-sponding to MP, although he was not yet ableto discriminate between problem types. Rickymastered solving MP after just four interven-tion sessions. In his second probe he main-tained performance on MP problems, but sim-ilarly to probe 1, was not able to discriminate.Ricky was able to master discriminating be-tween the two problems and met mastery cri-teria after four intervention sessions. In thethird probe he maintained performance onsteps of problem solving and was able to dis-criminate between problem types.

Kelly had a stable and consistent baselinefor MW (average six steps) and MP (average 0steps) across seven sessions. She was immedi-ately able to increase independent responsesafter receiving instruction on solving MWproblems and reached mastery criteria inthree sessions. She maintained performanceon MW problems in the first probe and hadminimal generalization to MP problems. Shewas unable to discriminate between problemson the second probe. She mastered MP prob-lems after three intervention sessions andmaintained responding for MP problems onthe second probe, although correct responseson MW decreased and she was unable to dis-criminate between problem types. Kelly wasable to reach mastery criteria for discriminat- T

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ing between problem types after three inter-vention sessions and maintained respondingduring the third probe.

Marc had a stable and consistent baselinefor MW (average two steps) and MP (average0 steps) problems across eight sessions. Fol-lowing two sessions of modeling for MW prob-lems he demonstrated an immediate jumpand increasing trend in independent respond-ing to the task analysis although this was aslower progression than other participants,taking 10 sessions to reach mastery criteria. Inthe first probe Marc maintained problem solv-ing for the MW problem type, although therewas some variability to his responding it didnot overlap with baseline. He was not able todiscriminate between problems. Marc’s per-formance during MP intervention plateauedafter nine sessions. He consistently had diffi-culty with independently discriminating theproblem as MP and saying the correspondingrule and operation. In the 10th missing-partintervention sessions, researchers providedMarc with a highlighter and showed him howto highlight the “x” on the graphic organizer,which then assisted him in determining therule, operation, and label. This additional vi-sual support resulted in an immediate in-crease in independent responding on thesesteps and therefore allowed him to reach mas-tery criteria following the eleventh session.Interventionists made the highlighter avail-able to Marc in all subsequent sessions. In thesecond probe Marc was able to maintain re-sponding to both missing-whole and missing-part problems, although neither were at mas-tery level and he did not discriminate betweenthe two. Marc mastered discriminating be-tween problem types after four interventionsessions. His performance on the final probewas variable for both problem type, althoughit was well above baseline, and he was able todiscriminate between problem types on threeout of four problems for two sessions.

Discussion

The purpose of this study was to evaluate theeffects of MSBI on mathematical problemsolving by students with ASD. Participantswere taught to solve group problems that re-quired algebraic reasoning using MSBI, whichincluded explicit instruction, an 8-step task

analysis, a graphic organizer, and visual sup-port for writing an equation. A functional re-lation was found between MSBI and problemsolving. Non-parametric effect size (Tau-U)confirmed visual analysis of a large effect(.87). All participants were able to discrimi-nate between problem types to choose thecorrect operation and solve problems.

Mathematical problem solving is a pivotalskill, yet it has not received as much attentionfrom researchers as basic and discrete appli-cations of mathematics (King, Lemons, & Da-vidson, 2016; Spooner et al., 2018). Althoughalgebra is a distinct domain of mathematics,algebraic reasoning is required for fluencyand conceptual knowledge across domains(Van de Walle et al., 2016). Students withASD/ID face several barriers to becoming flu-ent “algebra problem solvers”, including work-ing memory, executive functioning, and lan-guage, and require explicit learning strategyinstruction to access this general curriculumcontent.

The results of this study make an importantcontribution to the field’s knowledge on theapplication of evidence-based practices toteach mathematical problem solving to stu-dents with ASD/ID. MSBI incorporates severalestablished evidence-based practices, includ-ing systematic instruction, graphic organizertraining, and explicit instruction (Spooner,Root, et al., 2018). These findings extend theboundaries on generalization of problem solv-ing established by prior MSBI studies, includ-ing successful application of the strategy tomore complex domains of mathematics (i.e.,algebra) and calculation of double digitnumbers. In addition, the results of thisstudy highlight the need for explicit discrim-ination training.

Solving mathematical word problems re-quires simultaneous employment of multiplecomplex skills to select and execute an appro-priate strategy based on correct interpretationof the problem (Jitendra et al., 2015), includ-ing working memory and executive function-ing (Rockwell et al., 2011). Participants in thecurrent study were tasked with solving twosimilar yet distinctly different problems; thediscrimination was based on whether theproblem was asking for the size of the total orthe size of one of the parts. The task analysismade this difference salient as the participants


mapped the known information on thegraphic organizer, indicated what was un-known with an x, and said the correspondingrule. However, all participants in the studyrequired explicit discrimination training inorder to independently discriminate betweenMW and MP problems. Prior to explicit dis-crimination training, they overgeneralizedproblem solving and approached each prob-lem as the most recently taught type. For ex-ample, in probe two all participants solvedeach problem as a MP, as that was what wasmost recently taught, even though they haddemonstrated mastery on MW problems inprobe one. The findings from this research fillan important gap in the research highlightedby the findings of Root and Browder (2017),where participants had difficulty discriminat-ing between MW and MP problem types whenpresented concurrently and not provided withexplicit discrimination training.


The ability to solve word problems is an im-portant skill for all students, as it lays a foun-dation for reasoning and problem solving thattranslates to real-world scenarios and moreadvanced levels of mathematics (Powell &Fuchs, 2014; Van de Walle et al., 2016). Forstudents with ASD/ID who are having diffi-culty in solving word problems, MSBI can sup-port them in conceptually understandingwhat is happening in the problem and sup-port their procedural knowledge of how toarrive at a solution. While traditional schema-based instruction is an evidence-based prac-tice for students with high incidence disabili-ties, practitioners can consider their students’abilities and determine if they would benefitfrom the additional support provided throughMSBI. For example, in MSBI, a task analysis isused instead of a mnemonic (e.g., RUNS orFOPS) as a heuristic for solving the problem.Students who have limited early literacy skillsand cannot equate the R in the “RUNS” mne-monic with the R in “Reading”, or those whoneed complex steps such as “name the prob-lem type” further broken down into compo-nent steps, may benefit from the task analysisthat pairs considerate text with pictures. Inaddition, use of a task analysis in mathematicspromotes self-management, an evidence-

based practice for students with ASD (Barnett& Cleary, 2016; Wong et al., 2014).

Finally, practitioners can incorporate explicitdiscrimination training into instructional rou-tines; otherwise students may overgeneralizeproblem solving strategies. When teaching two-choice tasks (i.e., MW and MP), Engelmann andCarnine (1982) suggest teaching the first con-cept to mastery (i.e., MW), then the secondconcept to mastery (i.e., MP), before teachingstudents to discriminate between the two con-cepts.

Limitations and Future Research

There are several limitations to this study thatsuggest the need for future research. First, thisstudy only addressed one type of additiveproblems (i.e., group). Second, the problemsin this study followed a highly structured andpredictable format, and did not contain anyextraneous information. Third, although theword problems did address quantities largerthan 10, regrouping was not required. Futureresearch could evaluate the feasibility and ef-fectiveness of MSBI to solve and discriminatebetween problems of multiple problem typesthat include extraneous information and re-quire additional computational skills, as isgenerally found in general education mathe-matics texts. In addition, while this study hasconstruct validity and addresses a socially im-portant skill, the lack of direct social validitymeasure from the teacher or participants is alimitation. Finally, it is unknown whether theparticipants in this study were able to gener-alize their knowledge to real-world mathemat-ical problem solving scenarios that involve al-gebraic reasoning. Future research shoulddirectly measure, and if necessary teach, gen-eralization of problem solving skills to authen-tic real-world contexts, such as in daily living,leisure, or vocational settings.

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Received: 18 May 2017Initial Acceptance: 12 July 2017Final Acceptance: 11 September 2017


Juliet E. Hart Barnett and Kelly J. Whalon, Editors2014 ISBN 978-0-86586-490-0

The eighth volume of the CEC Division on Autism and Developmental Disabilities’Prism series, Friendship 101 focuses on building social competence, friendshipmaking, and recreation and leisure skills among students with autism spectrumdisorder and other developmental disabilities. Chapters in this evidence-based, user-friendly guide address the needs of students in different developmental periods (frompre-K through young adulthood), providing teachers, parents, faculty and teachereducators with tools and strategies for enhancing the social skill development of thesechildren and youth. Presented through an ecological perspective, together thesechapters emphasize building social competence within and across school, home, andcommunity contexts.

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