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Pre-print. Please see the published book chapter, or contact the author before citing:
Moeller, J., Spicer, J., Salmela-Aro, K., & Schneider, B. (2017). Advances in the research on
situation-specific and contextual aspects of student engagement. In: I. Schoon & R. K. Silbereisen
(Eds.). Pathways to Adulthood. Educational opportunities, motivation and attainment in times of
social change (pp.119-136). London: UCL IOE Press.
Advances in the research on situation-specific and contextual aspects of student engagement
Julia Moeller, Justina Spicer, Katariina Salmela-Aro and Barbara Schneider
Abstract
Research on student engagement often uses diverse definitions and measures, which can
lead to confusion in the understanding and application of findings. As previous reviews of the
literature have shown, these measures can overlap with other constructs, such as motivation, but
also can differ in other regards. This chapter responds to the research reviews that call for a more
fine-tuned approach in capturing and integrating the measurement of engagement. We first
provide an overview of the research organized into three components of engagement: (1) person-
specific, (2) situation-specific, and (3) contextual determinants. Then we discuss how these
different aspects of engagement can be investigated. We conclude by describing methods for
measuring engagement in situation- and context-specific settings as well as offer future directions
for research.
Keywords: engagement, situational interest, motivation, learning context, person-oriented approaches, Flow, demands-resource model, experience sampling method, repeated measures, profiles
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Interdisciplinary perspectives of engagement
Student engagement is a key concept in understanding optimal learning motivation and
behavior, and a critical component in addressing other mechanisms such as increasing
academic motivation and preventing school dropout. The research on engagement is
interdisciplinary and has been summarized in several review articles and handbooks (e.g.,
Eccles and Wang, 2012; Fredricks, Blumenfeld, and Paris, 2004; Jimerson, Campos, and
Greif, 2003; Lawson and Lawson, 2013; Christenson and Reschly, 2012). These reviews have
found that the term ‘student engagement’ is used as a broad and multifaceted construct.
Student engagement is influenced by characteristics of the context (e.g., societal gender
stereotypes), person (e.g., academic self-concept) and situation (e.g., being presented with a
challenge). The research on engagement is partly motivated by the hope that studying the
malleable aspects of student motivation can enhance our understanding of how students feel,
think, and act towards learning tasks and school in general, with the goal to support the
students’ learning processes. However, the malleable, situation-specific aspects of
engagement are often mentioned but understudied and not well integrated in models and
measures of student engagement. The present chapter addresses these issues. First, we give an
overview about the research on person-specific, situation-specific, and contextual
determinants of student engagement, then we show how these engagement determinants can
be investigated, and finally we describe methods for measuring situation- and context-specific
aspects of student engagement.
General definition of engagement
Student engagement is a term that summarizes students’ characteristics that are
beneficial for their learning, achievement, adjustment in school, and educational pathways.
Engagement is thus a socially desired outcome. Student engagement is not a theoretically
bound, clear-cut entity (Eccles and Wang, 2012; Reschly and Christenson, 2012), but rather a
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multifaceted construct for which many different definitions, components, and measures have
been suggested. Most authors agree that student engagement includes cognitive, emotional,
and behavioral components (Fredricks et al., 2004). This, however, is a rough distinction since
these components are often entangled and interdependent (see Damásio, 1994).
Student engagement is considered a malleable and partly situation-dependent form of
learning motivation and behavior, something that, if enhanced, is supposed to ameliorate
academic outcomes (Fredricks et al., 2004; Lawson and Lawson, 2013). Aspects of
engagement can also be distinguished by the specific content they refer to (e.g., test
engagement, classroom engagement, and extracurricular engagement, see Lau and Roeser,
2002), or by their sources of variation and determinants such as contextual, personal and
situational engagement (Fredricks et al., 2004). Here it is argued, that in order to understand
why individuals engage in or disengage from specific tasks, and how such short-term
engagement eventually crystalizes to stable behavioral patterns, we must study in-the-moment
components of engagement.
How is engagement different/similar compared to other multifaceted constructs of
motivation?
Researchers, educators, and educational policy makers have made engagement an
omnipresent term. Unfortunately, this has created much confusion about its meaning. Despite
several reviews systematizing the definitions and measures of engagement, Reschly and
Christenson (2012) point out that there is still confusion due to terminological ambiguities
such as labeling different phenomena with the same term (‘engagement’) and using different
labels to describe the same phenomenon. To remedy such terminological ambiguities, we
discuss some overlaps between engagement and other multifaceted constructs of motivation.
Passion, similar to engagement, is a multifaceted construct of socially desired learning
motivation (for a review see Moeller, 2014). According to an often-cited definition, passion
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describes the inclination of a person towards an activity that a person likes, finds important,
invests time and energy in, and with which the individual identifies (Vallerand et al., 2003).
Passion and engagement overlap in the components of commitment and investment of
resources/effort (see Fredricks et al., 2004), flow (see Shernoff, Csikszentmihalyi, Schneider,
and Shernoff, 2003), and energy (see Vallerand et al., 2003 and Salmela-Aro and Upadaya,
2012). One difference between school engagement and passion is that passion typically refers
to one specific topic or type of activities (e.g., playing soccer and doing activities related to
playing soccer), whereas school engagement can refer to a large variety of topics and
activities, if they are learning-related. The literature on passion and engagement furthermore
differ in their emphasis on subgroups of beneficial versus harmful forms of motivation. The
passion research mainly focuses on differences between adaptive, beneficial forms of passion
(‘harmonious passion’) and maladaptive, harmful forms (‘obsessive passion’), while the
engagement literature mostly focuses on the positive forms and outcomes of the construct.
However, both student engagement and harmonious passion co-occur with negative
experiences in many individuals, as recent studies have shown (Moeller, Keiner, and
Grassinger, 2015; Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, 2016; Salmela-Aro
and Upadyaya, 2014; Tuominen-Soini and Salmela-Aro, 2014; Wang and Peck, 2013).
Intrinsic motivation is another multifaceted psychological construct that describes
reasons why individuals voluntarily engage in activities without external constraints or
rewards (Ryan and Deci, 2000). Intrinsic motivation comprises different facets, such as
interest, flow and positive emotions, among others, and thus overlaps strongly with
engagement. A difference between both constructs is that school engagement also includes
aspects beyond intrinsic motivation (cognitive concentration and investment, behavior in
accordance to school norms disregarding of the motives, persistence in frustrating, effortful
learning activities), which is known to be important in the development of excellent skills
(Ericsson, Krampe, and Tesch-Römer, 1993, Ericsson and Charness, 1994).
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Research gaps regarding state aspects, stable aspects and contextual aspects
While many studies use the term engagement broadly, there is a lack of studies on the
more specific aspects of engagement, particularly situation- and domain-specific measures
(Fredricks and McColskey, 2012). Also lacking are theoretical models to specify the distinct
contribution of specific components of engagement to relevant outcomes and models of the
mechanisms through which situation-, person- and context-specific aspects of engagement
influence the addressed outcomes. To compare the influence of different contexts on student
engagement, it is desirable to have a conception of engagement that can be applied across
different contexts and yet captures context-specific characteristics.
The research on student engagement largely addresses correlations between
engagement and other variables (e.g., dropping out), but as Fredricks and McColskey (2012)
argue, this focus might ignore differently functioning subgroups. The few approaches that
have analyzed subgroups have contributed to our understanding of engagement dramatically.
For example, despite the negative correlation between student engagement and burnout
(Salmela-Aro and Upadyaya, 2014), recent studies found that up to one out of four Finnish
high school students were simultaneously highly engaged and exhausted (Salmela-Aro et al.,
2016; Tuominen-Soini and Salmela-Aro 2014). These students were disproportionally likely
to become disengaged at later time points. These findings imply that highly motivated
students are not necessarily those the teacher does not need to worry about, but that on the
contrary highly engaged students might need specific support to acquire and maintain their
resources and motivation. This and further studies have shown how important it is to consider
‘the dark side’ of high engagement in combined models of study demands and resources (see
also Salmela-Aro et al., 2016; Salmela-Aro and Upadyaya, 2014).
Investigating engagement
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Comprehensive investigation of engagement includes several determinants, such as
person-specific factors that contribute to students’ engagement; the specific situation students
experience; and the context of the task or activity. This section discusses these three aspects of
the learning environment.
Inter-individual variation: stable person-specific aspects of student engagement
Some students are generally more intensively, and/or more often engaged in school-
related tasks than other students, despite fluctuations between specific situations or
experiences (Tuominen-Soini and Salmela-Aro, 2014). That is, the level of a student’s
engagement remains partially stable across situations/contexts, which is labeled the ‘person-
specific’ component (Salmela-Aro and Upadyaya, 2014). Examples of these stable
components include identification with school norms (Finn, 1989; Voelkl, 1997) and goal-
orientation (e.g., the pursuit of education-related long-term goals). Furthermore, individuals
differ in their dispositional positive affect in their everyday life, meaning some individuals
experience generally more and more intense positive emotions (Naragon and Watson, 2009),
and are thus also more likely to feel happy, excited, and active in school, which are aspects of
school engagement. Moreover, engagement and achievement are influenced by other
relatively stable individual characteristics, such as the academic self-concept and subjective
task values (Wang and Eccles, 2013), individual symptoms of burnout (Salmela-Aro and
Upadyaya, 2014), and ethnic background (Johnson, Crosnoe, and Elder, 2001), among others.
These influences help to explain why engagement remains partially stable within individuals
across situations, contexts and long time spans, even though engagement includes
nevertheless also malleable, fluctuating aspects (see below).
Intra-individual variation: situational aspects of engagement
Another characteristic of engagement is that it is malleable with situation-specific
components (Fredricks et al., 2004; Lawson and Lawson, 2013). These malleable components
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include the reasons why an individual approaches a task and how these tasks are experienced.
When an individual approaches a new task for the sake of exploration it is called situational
interest. This is a mostly externally triggered state in which the individual alertly focuses their
attention on the task (Bloom, 1985; Sloboda, 1990; Sosniak, 1990). The situational interest in
a task is an essential element that may lead to rewarding task experiences, such as feelings of
success, mastery, and confidence (Gruber, Gelman, and Ranganath, 2014), which may build
future motivation and engagement. Triggering situational interest may give instructors
opportunities to facilitate the development of engagement. Capturing the situational aspects of
engagement provides useful information to the specific mechanisms that can explain what
exactly goes on in the mind when students feel attracted to approach a task.
Contextual variation of student engagement
Contextual determinants of student engagement are of particular interest for teachers
and educational policy makers, for if identified, these conditions could be optimized to
increase students’ engagement. In their overview of the research on contextual determinants
of engagement, Fredricks et al. (2004) summarized that student engagement is higher in
schools and classes that provide students with opportunities for voluntary choice, participation
in school policy, and cooperation. Engagement can be encouraged by setting clear and
consistent goals, placing students in small classes, and holding students accountable for rule-
breaking. Regarding school and classroom characteristics, Wang and Eccles (2013) found that
contextual characteristics influence different facets of engagement (emotional, cognitive,
behavioral) differently, which makes it necessary to systematically distinguish between facets
of engagement when studying the context effects on engagement.
Broader contextual influences such as gender stereotypes and discrimination, cultural
norms, and social inequalities, problems related to family formation, childrearing, and work-
home balance can also influence student engagement, particularly among certain subgroups
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and in specific domains, as for example the achievement motivation of women in STEM
(science, technology, engineering, math) fields, where societal gender stereotypes about math
abilities, both positive and negative, can be further reinforced at home by parents (Wang and
Degol, 2013).
While prior research highlights these domain-specific inter-individual differences in
engagement, a gap in the engagement literature seems to be the question how engagement can
be measured in regard to specific domains (Fredricks and McColskey, 2012); and how and
why different domains or school subjects differ in terms of the engagement they elicit. It is
argued that some subjects like physics or math are experienced as particularly difficult and
frustrating by many students and by particular student subgroups (e.g., girls), and that the
detrimental effects of the person characteristics (e.g., gender) on student engagement are a
main reason for the recruitment problems of engineering and natural science sectors. As Wang
and Degol (2013) depicted in their review, important contextual influences for science
motivation and engagement are school and class characteristics, teacher characteristics, family
characteristics, peer influences and sociocultural influences.
Support provided by teachers is an important determinant of students’ engagement
(Fredricks et al., 2004; Wang and Eccles, 2013). Teachers can promote engagement by
supporting the students academically, emotionally and/or by supporting their autonomy while
learning. Teachers can also influence their students’ engagement by presenting topics in an
interesting way, setting challenging goals without overburdening the students, and providing
immediate and clear feedback. The teachers’ goals and mindsets, differential expectations and
stereotypes, and the students’ interpersonal relationships to teachers also contribute to
engagement at school (Wang and Degol, 2013).
Peers can influence engagement. Peer groups with pro-school and pro-learning
attitudes can promote engagement activities that are valued by their peers and give them
opportunities to relate to their peer group. However, this effect of peer’s values and attitudes
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on student engagement can be domain-specific and even detrimental for contrasting school
subjects. For example, peer support for math- and science-related school subjects
strengthened girls’ motivation in the same subject, but was negatively related to the same
girls’ motivation in English (Leaper, Farkas, and Brown, 2012). These contrasting effects
suggest that a domain-specific perspective is useful to understand the mechanisms behind peer
support and achievement motivation.
Integrating the analyses of person-specific, situational, and contextual aspects of student
engagement
One theory that helps to understand the situational, and contextual aspects of
engagement is flow theory (Csikszentmihalyi, 1990). Flow describes optimal learning
moments, i.e., situations when an individual concentrates so deeply on a task that the
cognitive resources are strongly loaded, and other cognitive processes such as self- and time-
monitoring are temporarily suspended. Flow experiences are often described as the feelings of
absorption and that ‘time was flying by’, which are also central components of engagement
measures (Salmela-Aro and Upadaya, 2012). In flow, individuals typically feel in control of
the current task, the activity is running smoothly, they know at all moments which current
steps they should take to master the challenge of the task, and feel completely lost in their
thoughts (Engeser and Rheinberg, 2008). Flow contributes to learning motivation because it is
a rewarding experience (Weber, Tamborini, Westcott-Baker, and Kantor, 2009), which elicits
positive affects (happiness, etc.), motivates students to concentrate deeply in a task at hand,
incentivizes the desire to re-engage in similar experiences in the future, and leads to a
productive outcome (e.g., learning).
Flow typically occurs in situations where an individual’s skills and level of challenge
are in balance— meaning the activity is cognitively demanding, but not overly difficult
(Csikszentmihalyi and Schneider, 2000). This conceptualization of a profile combining high
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levels of challenge and skill is different from many other approaches, which often
operationalize engagement as a composite score (e.g., average) across all its indicators, where
a high score in one variable compensates for low scores in other variables. Schneider et al.
(2016) added a third component to this conception, situational interest, defining situational
engagement as moments in which a student experiences high levels of skill, challenge, and
interest. The authors found that these states of situational engagement were correlated at the
same time with positive emotions as well as feelings of stress and anxiety.
The demands-resource perspective addresses similar predictors of engagement as the
flow theory, namely resources (flow: skills) and demands (flow: challenges; Bakker and
Demerouti, 2007; Tuominen-Soini and Salmela-Aro, 2014; Salmela-Aro and Upadyaya,
2014). Previous studies applied typically rather stable measures of demands (e.g., goal
challenge) and resources (e.g., self-efficiency) and found that study-related resources and
personal resources both predicted later schoolwork engagement, while school burnout was
predicted by previous study demands. Both school engagement and school burnout were
relatively stable, but school burnout had a detrimental effect of school engagement at a later
time point (Salmela-Aro and Upadyaya, 2014).
Recent studies on the intra-individual profiles of engagement and burnout and on their
intra-individual relations to demands and resources indicated that high engagement was a
positive experience only for some students, while other students experienced high engagement
together with strong exhaustion, burnout, and negative emotions (Salmela-Aro et al., 2016;
Tuominen-Soini and Salmela-Aro, 2014). Adult employees who experienced such co-
occurrences of high work engagement and high burnout also reported high levels of demands
(e.g., workload, cumbersome bureaucracy) and resources (e.g., self-efficacy, supervisor
support). Although the latter findings need to be replicated in the school context to become
relevant for the school engagement research, we can conclude that examining the intra-
individual profiles of engagement and burnout as well as demands and resources may help to
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understand and integrate the interactions of positive and negative experiences in relation to
engagement.
New directions in the assessment of engagement
The following paragraph discusses measures of engagement that capture the situation-
specific and domain-specific aspects and determinants of engagement. Furthermore, we
discuss how to operationalize the challenge-skill and demands-resources balance that are a
necessary pre-condition of (situational) engagement.
How to assess person-specific aspects of situational engagement
One of the shortcomings in most assessments of engagement is the fact that the
typically used measures do not tap well the malleable and domain-specific aspects of
engagement (Fredricks and McColskey, 2012). However, it is crucial to assess the situation-
specific fluctuation if the malleable aspects shall be addressed, and particularly if the
situational determinants of engagement shall be identified. Some measures assess aspects that
are situation-specific in theory, such as absorption and other aspects of flow, but with
questions that are administered only once to the students and therefore require the respondents
to aggregate in their minds how often they experience the questioned states (see Salmela-Aro
and Upadaya, 2012). This is a way to assess a general tendency in the overall frequency or
intensity in the experience of situational engagement aspects, but this measure is not suited to
tab situation-specific or domain-specific variation, which makes it difficult to study for
instance how particular instruction strategies or changes in classroom settings affect the
respective state aspects.
Because teaching and learning vary across subject (Stodolskey, 1988), it is important
to measure and contextualize engagement in domain-specific settings. Domain-specific
measures can help to answer questions such as how science engagement can be fostered
among students, which is of major interest for many researchers (see Wang and Degol, 2013).
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One possibility to assess domain-specific aspects of engagement is the use of scales that were
developed to measure specific subcomponents of domain-specific motivation. For example,
the Programme for International Student Assessment (PISA) measured engagement-related
aspects of motivation in relation to specific school subjects, with scales that in certain years
included subject-specific items for enjoyment, future-related motivation, general value, and
competence beliefs (OECD, 2007). These scales are translated into many languages and help
facilitate the study of student engagement in different countries. Furthermore, the
measurement properties of the scales and items are validated and well-documented. These
PISA engagement scales tap aspects of personal interest in given subjects and topics, but also
aspects of intrinsic and utility values per the expectancy-value theory, all of which are key
components of school engagement.
Lau and Roeser (2002) measured content-specific aspects of situational engagement in
science by administering questionnaires to the students shortly after they engaged in the
respective activities. The authors distinguished between situational test engagement (e.g.,
students’ mood and energy level, use of cognitive strategies and effort expended during the
test), situational classroom engagement (e.g., students’ self-reports of how much attention
they paid in class, their degree of participation in science experiments, the amount of
homework they completed for the class, and their use of self-regulatory strategies, when
studying science) and situational extracurricular engagement (e.g., student’s self-reports of
how often they engaged in various science-related activities outside school during their free
time, such as reading books and magazines, visiting web sites, or watching TV programs
related to science).
D’Mello et al. (2014) applied another measure of situational engagement by video-
recording their participants during a relevant learning activity and asking them at the end of
the learning session to watch their recorded video and to judge in retrospection which of the
emotions on a selected list described their experience best. The list included the experiences
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anxiety, boredom, confusion/uncertainty, curiosity, delight, engagement/flow, frustration,
surprise, and neutral, and a description for each of these experiences. Engagement was
defined to the participants as a state of interest that results from involvement in an activity.
Experience Sampling Method
One approach to measure situational engagement is the Experience Sampling Method
(ESM) developed by Csikszentmihalyi, Larson, and Bruce (1977), which attempts to capture
the malleability of engagement by contextualizing the experience of the individual. The ESM
provides a way of capturing the moments of an individual’s daily life—immediate activities
and emotions at random intervals. As opposed to collecting data at one time point and relying
on the study participant to recall/recount their engagement over multiple situations, the ESM
beeps or notifies a person to respond to a series of questions that take about one minute to
answer and asks for instances which activity the individual is doing (open-ended) and how he
or she feels about it (scaled item). The participants respond multiple times over the course of a
week or two to construct a more comprehensive dataset of how experiences (e.g., situational
engagement) vary. To study both short-term and long-term change in engagement, ESM
assessment weeks can be included in long-term longitudinal studies, for instances with an
ESM week in years one, three, and five as conducted by Schneider (1992-1997).
Several studies have established measures for separated components of situational
engagement, such as the measurement literature on situational interest (Chen, Darst, and
Pangrazi, 2009; Linnenbrink-Garcia et al., 2010; Schiefele, 2009); flow (Csikszentmihalyi and
Schneider, 2000; Engeser and Rheinberg, 2008); and state affect (Pekrun et al., 2011; Watson,
Clark, and Tellegen, 1988). The measures developed in these separated research lines can be
combined to tap several specific components of situational engagement at a time. However,
using the full battery of combined multi-item measures would be impractical in ESM studies,
since the burden of repeated measures requires short questionnaires. Therefore, ESM typically
assesses each component of engagement with a single item or very few items. With the
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advancement of technology and the ability of ESM to be used with smartphones including the
possibility of augmenting the responses with additional data such as heart rate, there are rich
opportunities for understanding the individual experience.
While there are certainly strengths to using the ESM, it is not without critique.
Fredricks and McColskey (2012) argue that the necessarily short and economic ESM
measures are often very limited to a few specific aspects of engagement and do not come up
to the whole spectrum of the multifaceted construct. The data collected at multiple points of
the day over multiple days can also be more burdensome for the respondent, which can lead to
selection bias and attrition in the study sample (Scollon, Kim-Prieto, and Diener, 2003). The
application of this method for studying student engagement in school could also be disruptive
for learning that is being measured. Although ESM collects real-time data, the data are still
self-reported and subject to the same limitations of any self-reported data, even though their
concurrent, discriminant and predictive validities were found to be good (Hektner, Schmidt,
and Csikszentmihalyi, 2007).
How to assess the balance between demands (challenge) and resources (skill)
If situational demands outweigh the individual’s current resources to deal with these
demands, then this can lead to a stressful state that the flow theory would identify as ‘anxiety.’
Other imbalances between situational demands and resources can lead to experiences of
relaxation (low demand and high resources) and apathy (low demand and low resources) —
all of which differ strongly from each other and from more balanced states (Csikszentmihalyi
and Csikszentmihalyi, 1988). It is therefore important to differentiate between the different
profiles of demands-resources balance in state-measures of engagement. One distinction
between such types of states was described by Csikszentmihalyi and colleagues
(Csikszentmihalyi and Csikszentmihalyi, 1988; Csikszentmihalyi and Schneider, 2000), who
operationalized flow-states as coincidence of flow pre-conditions (high skills and high
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challenges) by creating dichotomous dummy variables (1 = flow; 0 = no flow), where 1
represents states in which all indicators (here: skills and challenges) scored highly, and 0
represents all other combinations (e.g., high challenge and low skills). In future studies, this
dichotomization between high and low scores of flow indicators (here: challenge and skills)
should be done based on raw scores, and not as in previous studies based on intra-individual
z-scores, because otherwise ratings below the scale midpoint (i.e., ‘rather not’ statements)
may be categorized as ‘high ratings’ in some individuals (Moeller, 2015).
Discussion
New insights provided by situation-specific measures
Student engagement is a hot topic for motivation researchers, education policy makers,
funding agencies, and educators. The reasons for the broad interest on engagement are the
expectation that students’ motivation is an important and malleable precursor for students’
achievement, adjustment and well-being in learning institutions. Building on previous reviews
that argued student engagement research understudied the malleable aspects of engagement
(e.g., Fredricks and McColskey, 2012), this chapter provided an overview about different
methods to measure malleable and context-specific aspects of student engagement, and
directions for future research.
Situation-specific measures of engagement can show how much students live up to
their maximum of engagement in school settings as compared to other everyday life activities.
The more often applied cross-situational measures provide insights about inter-individual
differences in learning motivation, but situation-specific measures open a whole new field of
intra-individual comparisons of experiences in different contexts, which helps to find out for
instances which school subjects are more engaging for a given individual (or group of
individuals), and which specific lesson was more engaging than another.
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In addition to the contextual information, situational measures of engagement allow to
model short-term development, for example from day to day, which yields insights that
macro-time level scales cannot provide (Hamaker, Dolan, and Molenaar, 2005; Lichtwarck-
Aschoff et al., 2008; Mroczek, Spiro, and Almeida, 2003). One important feature of
situational measures is the possibility for the researcher to aggregate the cross-situational
frequency or mean level of engagement states across the repeatedly measured experiences in a
certain context. Compared to the prevailing cross-situational questionnaires that require
individuals to aggregate their ‘typical’ engagement across all experiences in their own minds,
the calculation by the researcher would be less biased by social desirability and memory
errors. Using repeated situational measures, the fluctuating state aspects and the rather stable
trait aspects of engagement can be disentangled with multilevel modeling of variance between
situations (within-level) and variance between individuals (between-level).
In addition, repeated measures with ESM can be used to model the influences of the
context on the engagement, and can be used to compare the different effects of different
contexts. This is an advantage compared to the prevailing cross-sectional questionnaires,
which are either relatively context-unspecific, or limited to only one or few contexts.
For the above discussed reasons, we assume that providing useful measures for
situational engagement is a necessary but not sufficient step on the way towards
understanding what can be done at school to optimize students’ motivation, achievement, and
well-being. The next necessary step would use these measures to identify the conditions for
optimal engagement and to evaluate interventions aiming at such optimization.
Subgroups of states and individuals
Prior research on student engagement relies predominantly on variable-oriented
approaches (analyses of covariation between variables), but increasingly, researchers have
pointed out that overall correlations between variables describe populations, but do not
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necessarily apply on the level of individuals (Bergman and Magnusson, 1997; Molenaar,
2004; Reizle, 2013). In response, recent studies have examined the differences between
subgroups of individuals and their engagement profiles, uncovering insights that would have
been overlooked with the correlation-based analyses. For example, using latent profile
analyses, Tuominen-Soini and Salmela-Aro (2014) described the unexpected subgroup of
engaged-exhausted students, who experienced high levels of both engagement and burnout
and were likely to become disengaged at later measurement time points. Similarly, situational
engagement can be accompanied by rather negative, anxious, demanding and stressful
experiences in some situations and students (Moeller et al., 2015; Schneider et al., 2016).
More research is needed to understand this potentially ‘dark side of student engagement’ and
the possibilities to help motivated students to maintain their engagement without getting
exhausted in this process.
Directions for future research
As previous studies concluded, the research on the multifaceted construct of
engagement requires models, definitions and measurements that distinguish between the
specific facets of engagement and their relation to the outcomes or predictors of interest (e.g.,
Eccles and Wang, 2012; Fredricks et al., 2004). In this chapter, we demonstrated why this
specification implies the necessity to disentangle more systematically the situation-specific,
person-specific and context-specific sources of variance of student engagement. Particularly
needed are theoretical models and empirical studies that would examine these three groups of
determinants jointly while distinguishing them systematically. Future studies should examine
these determinants’ unique and interacting influences on the engagement of students in order
to answer which determinant affects engagement for which students and under which
circumstances.
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Since self-reports might be biased by response styles or other contortions, future
studies should validate measures of situational engagement by investigating their relation to
and objective measures that go beyond self-reports, such as objective reports on achievement
outcomes (GPA, grades), psychophysiological data of e.g., stress and indicators of state affect,
or behavioral observations (see also the chapter by Cambria and Dicke in this volume).
Future studies should continue to look out for subgroups of students who experience
different patterns of engagement and burnout than the overall population-level trend suggests.
Understanding such person-specific or subgroup-specific patterns of engagement and burnout
is key to the individualized assessment and support that we ideally would want to see in
schools. The recent intra-individual findings suggest that interventions targeting optimal
learning motivation in schools might need to move away from a ‘one size fits all’ approach
towards the question of ‘what works for whom under which circumstances.’ Furthermore,
analyses of intra-individual profiles might also be interesting on the level of specific
situations, for instance if the in-the-moment profiles of skills and challenges and their
relations to emotions in the same learning situations are examined. We expect that these
approaches will not only reveal new theoretical insights, but may also help identifying the
students who are in need for support from their teachers and caregivers in order to learn how
to maintain and renew their resources and high levels of motivation without getting
overwhelmed, stressed, or burned out in the process.
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References
Bakker, A.B. and Demerouti, E. (2007) ‘The Job Demands-Resources model: State of the art’.
Journal of Managerial Psychology, 22, 309-328.
Bergman, L.R. and Magnusson, D. (1997) ‘A person-oriented approach in research on
developmental psychopathology’. Development and Psychopathology, 9, 291–319.
Berridge, K.C. (2007) ‘The debate over dopamine’s role in reward: The case for incentive
salience’. Psychopharmacology, 191(3), 391–431. doi:10.1007/s00213-006-0578-x
Bloom, B. (1985) ‘The nature of the study and why it was done’. In B. Bloom (Ed.),
Developing talent in young people (pp. 3–18). New York: Ballantine.
Chen, A., Darst, P.W., and Pangrazi, R.P. (1999) ‘What constitutes situational interest?
Validating a construct in physical education’. Measurement in Physical Education and
Exercise Science, 3(3), 157-180, doi:10.1207/s15327841mpee0303_3
Christenson, S.L., Reschly, A.L., and Wylie, C. (2012) Handbook of Research on Student
Engagement. New York: Springer.
Csikszentmihalyi, M. (1990) Flow: The psychology of optimal experience. New York: Harper
and Row.
Csikszentmihalyi, M., and Csikszentmihalyi, I. (1988) Optimal experience: Psychological
studies of Flow in consciousness. Cambridge: University Press.
Csikszentmihalyi, M., Larson, R., and Bruce, S.C. (1977) ‘The ecology of adolescent activity
and experience’. Journal of Youth and Adolescence, 6, 281-294.
Csikszentmihalyi, M. and Schneider, B. (2000) Becoming Adult. New York: Basic Books. Damásio, A. (1994) Descartes' Error: Emotion, Reason, and the Human Brain. New York:
Avon.
-
20
20
D’Mello, S., Lehman, B., Pekrun, R., and Graesser, A. (2014) ‘Confusion can be beneficial
for learning’. Learning and Instruction, 29, 153-170.
doi:10.1016/j.learninstruc.2012.05.003
Eccles, J.S. and Wang, M. (2012) ‘Part I commentary: So what is student engagement
anyway?’ In S.L. Christenson, A.L. Reschly and C. Wylie (Eds.) Handbook of
Research on Student Engagement. New York: Springer Sciences, 133-145.
Engeser, S. and Rheinberg, F. (2008) ‘Flow, moderators of challenge-skill-balance and
performance’. Motivation and Emotion, 32, 158-172. doi:10.1007/s11031-008-9102-4
Ericsson, K. A., and Charness, N. (1994) ‘Expert performance: Its structure and acquisition’.
American Psychologist, 49, 71–76. doi:10.1037/0003-066X.49.8.725
Ericsson, K., Krampe, R. T., and Tesch-Römer, C. (1993) ‘The role of deliberate practice in
the acquisition of expert performance’. Psychological Review, 100, 363–406.
doi:10.1037/0033-295X.100.3.363
Finn, J. D. (1989). ‘Withdrawing from school’. Review of Educational Research, 59, 117–
142.
Fredricks, J. A., Alfeld, C., and Eccles, J. S. (2010) ‘Developing and fostering passions in
academic and nonacademic domains’. Gifted Child Quarterly, 54(1), 18-30.
doi:10.1177/0016986209352683
Fredricks, J. A., Blumenfeld, P. C. and Paris, A. H. (2004) ‘School engagement: Potential of
the concept, state of the evidence’. Review of Educational Research, 74(1), 59–109.
doi:10.3102/00346543074001059
Fredricks and McColskey (2012) ‘The measurement of student engagement: A comparative
analysis of various methods and student self-report instruments’. In: S. L. Christenson,
A. L. Reschly, and C. Wylie (Eds.). Handbook of Research on Student Engagement
Springer: New York, 763-782.
-
21
21
Gruber, M. J., Gelman, B. D. and Ranganath, C. (2014) ‘States of curiosity modulate
hippocampus-dependent learning via the dopaminergic circuit’. Neuron, 84(2), 486–
496. DOI: http://dx.doi.org/10.1016/j.neuron.2014.08.060
Hamaker, E.L., Dolan, C.V., and Molenaar, P.C.M. (2005) ‘Statistical modeling of the
individual: Rationale and application of multivariate stationary time series analysis’.
Multivariate Behavioral Research, 40, 207-233.
Hektner, J. M., Schmidt, J. A., and Csikszentmihalyi, M. (2007) Experience Sampling
Method. Measuring the quality of everyday life. Thousand Oaks, CA, US: Sage
Publications.
Jimerson, S. R., Campos, E. and Greif, J. L. (2003) ‘Toward an understanding of definitions
and measures of school engagement and related terms’. The California School
Psychologist, 8, 7-28.
Johnson, M. K., Crosnoe, R., Elder, G. H. Jr. (2001) ‘Students' attachment and academic
engagement: The role of race and ethnicity’. Sociology of Education, 74(4), 318-340.
Lau, S. and Roeser, R. W. (2002) ‘Cognitive abilities and motivational processes in high
school students’ situational engagement and achievement in science’. Educational
Assessment, 8(2), 139–162.
Lawson, M. and Lawson, H. (2013) ‘New conceptual frameworks for student engagement
research, policy, and practice’. Review of Educational Research, 83, 432-479.
Leaper, C., Farkas, T., and Brown, C. S. (2012) ‘Adolescent girls’ experiences and gender-
related beliefs in relation to their motivation in math/science and English’. Journal of
Youth Adolescence, 41, 268–282.
Lichtwarck-Aschoff, A., van Geert, P., Bosma, H., and Kunnen, S. (2008) ‘Time and identity:
A framework for research and theory formation’. Developmental Review, 28(3), 370-
400.
-
22
22
Linnenbrink-Garcia, L., Durik, A. M., Conley, AM. M., Barron, K. E., Tauer, J. M.,
Karabenick, S. A., and Harackiewicz, J. M. (2010) ‘Measuring situational interest in
academic domains’. Educational and Psychological Measurement, 70(4), 647–671.
doi:10.1177/0013164409355699
Moeller, J. (2014) ‘Passion as concept of the psychology of motivation. Conceptualization,
assessment, inter-individual variability and long-term stability’. Dissertation published
online at www.db-thueringen.de/servlets/DerivateServlet/Derivate-
29036/DissJuliaMoeller.pdf
Moeller, J. (2015) ‘A word on standardization in longitudinal studies: don’t’. Frontiers in
Psychology, 6(1389). doi:10.3389/fpsyg.2015.01389
Moeller, J., Ivcevic, Z., White, A. E., Menges, J. I., & Brackett, M. A. (under review). Highly
engaged but about to quit: Intra-individual profiles of work engagement and burnout.
Manuscript submitted for publication.
Moeller, J., Keiner, M., and Grassinger, R. (2015) ‘Two sides of the same coin: Do the dual
’types’ of passion describe distinct subgroups of individuals?’ Journal for Person-
Oriented Research, 1(3), 131-150. doi:10.17505/jpor.2015.14
Moeller, J., Salmela-Aro, K., Lavonen, J., and Schneider, B. (2015) ‘Does anxiety in math
and science classrooms impair math and science motivation? Gender differences
beyond the mean level’. International Journal of Gender, Science, and Technology,
7(2), 229-254.
Molenaar, P. C. M. (2004) ‘A manifesto on psychology as idiographic science: Bringing the
person back into scientific psychology, this time forever’. Measurement:
Interdisciplinary Research and Perspectives, 2(4), 201-218.
doi:10.1207/s15366359mea0204_1
-
23
23
Mroczek, D. K., Spiro III, A., and Almeida, D. M. (2004) ‘Between- and within-person
variation in affect and personality over days and years: How basic and applied
approaches can inform one another’. Aging International, 28, 260-278.
Naragon, K., and Watson, D. (2009) ‘Positive affectivity’. In S. Lopez (Ed.), The
Encyclopedia of Positive Psychology. Hoboken, NJ: Wiley-Blackwell, 707-711.
OECD (2007) PISA 2006. Science Competencies for Tomorrow’s World (Volume 1:
Analysis). Paris: OECD.
Pekrun, R., T., Goetz, T., Frenzel, A. C., Barchfeld, P. and Perry, R. P. (2011) ‘Measuring
emotions in students’ learning and performance: The Achievement Emotions
Questionnaire (AEQ)’. Contemporary Educational Psychology, 36(1), 36–48.
doi:10.1016/j.cedpsych.2010.10.002
Reizle, M. (2013) ‘Introduction: Doubts and insights concerning variable- and person-oriented
approaches to human development’. European Journal of Developmental Psychology,
10(1), 1-8. doi:10.1080/17405629.2012.742848
Reschly, A. L., and Christenson, S. L. (2012) ‘Jingle, jangle, and conceptual haziness:
evolution and future directions of the engagement construct’. In: S. L. Christenson, A.
L. Reschly, and C. W. (Eds.). Handbook of Research on Student Engagement.
Springer, New York, 3-20.
Reschly, A. L., Huebner, E. S., Appleton, J. J., Antaramian, S. (2008) ‘Engagement as
flourishing: The contribution of positive emotions and coping to adolescents’
engagement at school and with learning’. Psychology in the Schools, 45(5), 419-431.
doi:10.1002/pits.20306
Rheinberg, F., Vollmeyer, R. and Engeser, S. (2003) ‘Die Erfassung des Flow-Erlebens’ [The
assessment of flow experience]. In J. Stiensmeier-Pelster and F. Rheinberg (Hrsg.),
Diagnostik von Motivation und Selbstkonzept. Göttingen: Hogrefe, 261-279.
-
24
24
Ryan, R. M. and Deci, E. L. (2000) ‘Intrinsic and extrinsic motivations: Classic definitions
and new directions’. Contemporary Educational Psychology 25, 54–67.
doi:10.1006/ceps.1999.1020
Salmela-Aro, K., Kiuru, N., Leskinen, E., Nurmi, J.-E. (2009) ‘School-Burnout Inventory
(SBI): Reliability and validity’. European Journal of Psychological Assessment, 25(1),
48–57. doi:10.1027/1015-5759.25.1.48
Salmela-Aro, K., Moeller, J., Schneider, B., Spicer, J., and Lavonen, J. (2016) ‘Integrating the
light and dark sides of student engagement with person-oriented and situation-specific
approaches’. Learning and Instruction, 43, 61–70.
doi:10.1016/j.learninstruc.2016.01.001
Salmela-Aro and Upadaya (2012) ‘The Schoolwork Engagement Inventory: Energy,
dedication, and absorption (EDA)’. European Journal of Psychological Assessment,
28(1), 60–67. doi:10.1027/1015-5759/a000091
Salmela-Aro, K., and Upadyaya, K. (2014) ‘School burnout and engagement in the context of
demands–resources model’. British Journal of Educational Psychology, 84, 137–
151.doi:10.1111/bjep.12018
Schiefele, U. (2009) ‘Individual and situational interest’. In: Kathryn Wentzel and Allan
Wigfield (Eds.). Handbook of Motivation at School (pp.197- 222). New York:
Routledge.
Schneider, B. (1992-1997) Sloan Study of Youth and Social Development, 1992-1997 [United
States].ICPSR04551-v1. Chicago, IL: University of Chicago, National Opinion
Research Center, Ogburn-Stouffer Center [producer] 1997. Ann Arbor, MI: Inter-
university Consortium for Political and Social Research [distributor], 2007-07-20.
doi:10.3886/ICPSR04551.v1
Schneider, B., Krajcik, J., Lavonen, J., Salmela-Aro, K., Broda, M., Spicer, J., Bruner, J.,
Moeller, J., Linnansaari, J., Juuti, K., and Viljaranta, J. (2016) ‘Investigating optimal
-
25
25
learning moments in U.S. and Finnish science classes’. Journal of Research in Science
Teaching, 53(3), 400-421.
Scollon, C. N., Kim-Prieto, C., and Diener, E. (2003) ‘Experience sampling: promises and
pitfalls, strengths and weaknesses’. Journal of Happiness Studies, 4, 5-34.
Shernoff, D. J., Csikszentmihaly, M., Schneider, B., and Shernoff, E. S. (2003) ‘Student
engagement in high school classrooms from the perspective of flow theory’. School
Psychology Quarterly, 18(2), 158-176.
Sirin, S. R. (2005) ‘Socioeconomic status and academic achievement: A meta-analytic review
of research’. Review of Educational Research, 75(3), 417–453
Sloboda, J. A. (1990) ‘Musical excellence—How does it develop?’ In M. Howe (Ed.),
Encouraging the development of exceptional skills and talents. Leicester, UK: British
Psychological Society, 165–178.
Sosniak, L. A. (1990) ‘The tortoise, the hare, and the development of talent’. In M. Howe
(Ed.), Encouraging the development of exceptional skills and talents. Leicester, UK,
149–164
Stodolsky, S. (1988) The Subject Matters: Classroom Activity in Math and Social Studies.
Chicago: University of Chicago Press.
Tuominen-Soini, H., Salmela-Aro, K. (2014) ‘Schoolwork engagement and burnout among
Finnish high school students and young adults: Profiles, progressions, and educational
outcomes’. Developmental Psychology, 50(3), 649-662.
Vallerand, R. J., Blanchard, C. M., Mageau, G. A., Koestner, R., Ratelle, C., Léonard, M., et
al. (2003) ‘Les passions de l'âme: On obsessive and harmonious passion’. Journal of
Personality and Social Psychology, 85(4), 756-767. doi:10.1037/0022-3514.85.4.756
Voelkl, K. E. (1997) ‘Identification with school’. American Journal of Education, 105, 204–
319.
-
26
26
Wang, M.-T. and Eccles, J. S. (2013) ‘School context, achievement motivation, and academic
engagement: A longitudinal study of school engagement using a multidimensional
Perspective’. Learning and Instruction 28, 12-23.
Wang, M.-T. and Degol, J. (2013) ‘Motivational pathways to STEM career choices: Using
expectancy–value perspective to understand individual and gender differences in
STEM fields’. Developmental Review 33, 304–340.
Wang, M.-T., and Peck, S. (2013) ‘Adolescent educational success and mental health vary
across school engagement profiles’. Developmental Psychology, 49, 1266-1276.
Watson, D., Clark, L. A., and Tellegen, A. (1988) ‘Development and validation of brief
measures of positive and negative affect: The PANAS Scales’. Journal of Personality
and Social Psychology, 54(6), 1063-1070.
Weber, R., Tamborini, R., Westcott-Baker, A., and Kantor, B. (2009) ’Theorizing flow and
media enjoyment as cognitive synchronization of attentional and reward networks’.
Communication Theory, 19(4), 397–422. doi:10.1111/j.1468-2885.2009.01352.x