Educating Students Across Locales - Education...

Educating Students Across Locales

Understanding Enrollment and Performance Across Virtual SchoolsOctober 2016

Written By: Kristen DeBruler & Jungah Bae, Michigan Virtual Research Institute™, with support from members of the Virtual School Leadership Alliance

Georgia Virtual School, The Virtual High School, Michigan Virtual School®, IDEAL-NM, North Carolina Virtual Public School, VirtualSC, and Wisconsin Virtual School

www.mvlri.orgOctober 2016 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Georgia Virtual School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

The Virtual High School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Michigan Virtual School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

IDEAL-NM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

North Carolina Virtual Public School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

VirtualSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Wisconsin Virtual School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

Table of Contents

Suggested Citation: DeBruler, K . & Bae, J . (2016) . Educating students across locales: Understanding enrollment and performance across virtual schools . Lansing, MI: Michigan Virtual University . Retrieved from http://media .mivu .org/institute/pdf/locale .pdf

http://media.mivu.org/institute/pdf/locale.pdf


Teaching in rural schools and educating rural students presents challenges unique to that locale . It is well known that population demographics are shifting and have been for some time, resulting in a migration out of rural areas . Families are drawn to suburbs, towns, and cities and take with them the necessary tax base to operate schools and provide educational services . Vander Ark, in a 2013 article on Getting Smart,1 cites that some 150,000 schools were closed in the last century . Vander Ark is not alone in his assessment of the challenges rural schools face; Nordine, writing for the Virtual School Leadership Alliance (VSLA),2 cites declining enrollments, high socioeconomically disadvantaged populations, high transportation costs, a lack of computer and internet access in homes, low teacher pay, and high teacher turnover as challenges faced by rural schools . These challenges, according to Nordine, start a cycle of low student achievement, perceptions of poor educational quality, and lack of confidence and trust in rural schools evidenced by failed referendums or bonds .

Rural schools with shrinking student populations, high teacher turnover, and low operating budgets are often unable to provide high-level courses or those which are not traditionally in high demand (i .e ., German, Japanese, American Sign Language) . Analyzing data available through the Ohio Department of Education, the Columbus Dispatch reported that suburban districts average 26 high-level courses, including upper level mathematics courses, Advanced Placement (AP) courses, and nontraditional foreign languages, while rural districts were only able to offer an average of 6 .5 high-level courses .3 Additionally, in their 2008 K-12 Online Learning survey of U .S . School District Administrators,4 Picciano and Seaman state that secondary students in rural schools are less likely to take AP science courses (6 .8% for rural compared to 26 .5% in city and suburb locales) . It is no surprise that rural schools on average are not able to offer as many high-level courses as suburban schools (and likely city and town as well); they lack both the minimum number of students to offer many of these courses and, more importantly, may not have teachers on staff certified in the specific subject area .

Failure, however, is not a foregone conclusion for rural schools . Distance education, specifically in the form of supplemental online learning, has been able to help rural schools fill in some of the gaps, and expand their educational offerings . Nordine cites online education as a way to break the cycle, mentioned earlier, of low student performance and declining funding . According to Nordine, online education can improve access to both high quality content and teachers, bringing with it equity and access to greater professional learning options .

In both their original report from 20075 and the follow-up from 2008,6 Picciano and Seaman state that a greater proportion of rural districts (compared to suburban and city) indicated enrolling students in distance education courses (46% for rural compared to 28% for suburb and 23% for city) . While the statistics are now nearly a decade old, it is reasonable that both the trends and the motivations behind enrolling students in distance education courses and, more recently online courses, remain similar given that rural students are under similar constraints as they were 10 years ago . Picciano and Seaman report that rural schools clearly articulated the importance of the availability of online courses in order to provide students more course choices in subject areas that have historically been difficult for rural schools to offer (advanced sciences, mathematics, and foreign languages) .

Picciano and Seaman highlight an interesting trend in online learning: a greater proportion of rural students take online courses than students in other locales . That is not to say that more rural students take online courses than students in other locales; this may or may not be the case given the low overall enrollments in rural schools . Another important issue related to enrollments but distinctly different is how well rural students do in their online courses .

1 http://gettingsmart .com/2013/01/how-online-learning-is-saving-and-improving-rural-high-schools/2 http://www .virtualschoolalliance .org/online-learning-lifeline-rural-schools/3 http://www .dispatch .com/content/stories/local/2014/11/30/rural-kids-get-fewer-ap-classes .html4 http://www .onlinelearningsurvey .com/reports/k-12-online-learning-2008 .pdf5 http://www .onlinelearningsurvey .com/reports/k-12-online-learning .pdf6 http://www .onlinelearningsurvey .com/reports/k-12-online-learning-2008 .pdf

Introduction

http://www.onlinelearningsurvey.com/reports/k-12-online-learning.pdf

http://www.onlinelearningsurvey.com/reports/k-12-online-learning-2008.pdf


The Michigan Virtual Learning Research Institute™ (MVLRI™), in Michigan’s K-12 Virtual Learning Effectiveness Report7 released in 2014, found that students in rural locales tended to perform on par with students from other locales in their online courses . Sixty-three percent of rural enrollments reached “completed/passed” status compared to 59% in towns, 63% in suburbs, and 64% in cities . The author of the report is careful to caution, however, that it is not simply a matter of rural students being better suited for online learning; rather, it is likely that the differences in performance can be attributed to local supports available to the student and, more specifically, the students’ reasons for taking the online course . This is an important distinction because the Effectiveness Report also found that students taking online courses to recover lost credits perform poorly compared to students taking the course as their first attempt at the credits . As mentioned previously, supplemental online learning offers rural students more learning options, and it seems plausible that rural students are taking advantage of the increased options made available by online learning and not strictly for credit recovery purposes .

With this in mind, researchers at MVLRI, in conjunction with our partners at the VSLA8 wanted to investigate both the enrollment and pass rates of not only rural students, but students in all locales, and see how well students performed across locales . While MVLRI reports annually on enrollment and completion/pass-rate trends (discussed below) both within the Michigan Virtual School® (MVS®)9 and statewide,10 we wanted to explore this nationally with help from our colleagues at the VSLA . Seven case studies from seven virtual schools across the United States are presented, exploring enrollment and completion/pass-rate trends . The report concludes with a discussion of the trends apparent across the virtual schools presented in this study .

A couple of important notes that will aid in interpreting and understanding the case studies . First, from this point forward, “completed/passed” rate will no longer be used . Instead two separate terms will be used to convey two distinct statistics: completion rate and pass rate . Completion rate will refer to the calculation of how many enrollments completed the online course, pass or fail . This calculation is presented only once in each case study, as an overall completion rate for the virtual school . While it may be tempting to compare completion rates across virtual schools, this is problematic as each virtual school has different procedures for calculating completion rate . For example, one virtual school allows for a two-week transition period; any withdrawals or drops during that period are not factored into the overall completion rate . Additionally, different virtual schools had varying procedures for assigning numerical scores to incomplete enrollments . Some did not assign a numerical score if the student did not reach a certain threshold of course points while others would record earned course points as a numerical score regardless of whether the student completed the course or obtained a minimum threshold of points . Therefore, completion rate is an internal calculation (we asked each virtual school to provide this statistic to the researchers) of how many students complete the course, designed to aid in interpreting another statistic: pass rate .

Apart from enrollment counts, pass rate is the primary statistic used in the case studies . Pass rate refers to how many completed (pass or fail) enrollments earned the minimum amount of available course points . For a majority of virtual schools, the minimum amount of course points needed to “pass” an online course was 60%; however, for two virtual schools it was 70% (this is noted in the specific case studies) . Again, given the issues with the determination and calculation of completion, only enrollments marked as complete were included in the pass rate analysis .

Second, virtual schools by and large do not typically assign “grades” upon completion of an online course . Instead they often report a score based on course points earned by a student compared to available course points . From there, it is up to the local school to assign the student a “grade” for that course given the local schools’ own

7 http://media .mivu .org/institute/pdf/er_2014 .pdf8 http://www .virtualschoolalliance .org/9 http://media .mivu .org/institute/pdf/RPT_MVU_Legislature_2015 .pdf10 http://media .mivu .org/institute/pdf/er_2014 .pdf

Introduction

http://media.mivu.org/institute/pdf/er_2014.pdf

http://www.virtualschoolalliance.org/

http://media.mivu.org/institute/pdf/RPT_MVU_Legislature_2015.pdf

http://media.mivu.org/institute/pdf/er_2014.pdf


grading scale . This is important to note because while virtual schools may have a passing threshold (typically either 60% or 70%), it is ultimately up to the school to determine the grade . Several virtual schools we worked with were careful to note that they do not make the final determination about whether or not a student passed a course — that lies with the local school . So while thresholds for “passing enrollments” are presented for each virtual school profiled here, in most cases it is ultimately up to the local school to award both credit and a final course grade .

Third, this report uses locale codes developed and identified by the National Center for Education Statistics (NCES) . These codes refer to the proximity of a school or district in relation to an urbanized area, defined by NCES as a densely settled core with densely settled surrounding areas . Districts are assigned locale codes based on the locale codes of the schools within the district and the percentage of students at each type of locale . There are 12 locale codes . (See Table 1, which includes information from the NCES website11 for more information on the definition and characteristics of each locale .) There were numerous enrollments that did not report a locale code . These enrollments are included in total enrollment and pass rate counts but are not (unless otherwise specified) included in detailed breakdowns as they do not represent a cohesive geographic group .

Table 1. Locale Code Definition and CharacteristicsLocale Definition

City, Large Territory inside an urbanized area and inside a principal city with population of 250,000 or more .

City, Midsize Territory inside an urbanized area and inside a principal city with population less than 250,000 and greater than or equal to 100,000 .

City, Small Territory inside an urbanized area and inside a principal city with population less than 100,000 .

Suburb, Large Territory outside a principal city and inside an urbanized area with population of 250,000 or more .

Suburb, Midsize Territory outside a principal city and inside an urbanized area with population less than 250,000 and greater than or equal to 100,000 .

Suburb, Small Territory outside a principal city and inside an urbanized area with population less than 100,000 .

Town, Fringe Territory inside an urban cluster that is less than or equal to 10 miles from an urbanized area .

Town, Distant Territory inside an urban cluster that is more than 10 miles and less than or equal to 35 miles from an urbanized area .

Town, Remote Territory inside an urban cluster that is more than 35 miles from an urbanized area .

Rural, Fringe Census-defined rural territory that is less than or equal to 5 miles from an urbanized area, as well as rural territory that is less than or equal to 2 .5 miles from an urban cluster .

Rural, Distant Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an urbanized area, as well as rural territory that is more than 2 .5 miles but less than or equal to 10 miles from an urban cluster .

Rural, Remote Census-defined rural territory that is more than 25 miles from an urbanized area and is also more than 10 miles from an urban cluster .

Finally, we understand that there is much that quantitative data cannot speak to regarding the unique context and accomplishments of each virtual school . We attempted to standardize as best we could and present the following data not as a way to compare the performance of virtual schools but rather to begin to understand how each serves students in different locales . Understanding this at a high-level will allow us to ask more specific questions, tailored exclusively to each virtual school . We begin to ask and answer some of these emerging questions in the discussion section following the case studies, but we still have many unanswered questions as you will see in the conclusion .

11 https://nces .ed .gov/ccd/rural_locales .asp

Introduction

https://nces.ed.gov/ccd/rural_locales.asp


About Georgia Virtual School Georgia Virtual School (GaVS) is a program of the Georgia Department of Education's Office of Technology Services . The program is accredited by the Southern Association of Colleges and Schools Council on Accreditation and School Improvement and operates in partnership with Georgia schools and parents to offer middle school and high school level courses across the state . GaVS provides a teacher-led, virtual classroom environment and also offers an online media center and a guidance center to support students throughout their online course experience . Students can choose from approximately 100 course offerings in the core content areas, world languages, Career, Technical and Agriculture Education (CTAE), electives, and a vast AP course selection .

GaVS Enrollment Descriptives GaVS had 15,162 completed course enrollments in the 2013-14 school year with an overall pass rate of 81 .7%12 and a completion rate of 99% . GaVS considers a “passing enrollment” one that earned at least 70% of the available course points . GaVS recorded enrollments in all but one of the locale codes identified by NCES, with approximately 65% of enrollments (in the 2013-14 school year) coming from large suburban districts . GaVS serves students both within and outside of Georgia, and as such there is a small set of enrollments without locale codes . These have been identified in the tables as “out-of-state .” The second largest enrollment group was the rural locale, accounting for approximately 18% of total enrollments in the 2013-14 school year . There were no tests run to determine statistical significance; however, enrollments from small cities recorded the lowest overall pass rate (73 .2%), and large cities recorded the highest overall pass rate (93%) . Given the large discrepancy in total enrollments between small cities and large (915 and 43 enrollments, respectively) and the pass rate of midsize cities, the overall pass rate for cities was 77 .5%, the lowest among all of the locale codes . Discounting out-of-state enrollments (which represented a very small percent of the total enrollments), the suburb region recorded the highest overall pass rate of 83%, comprised almost exclusively of enrollments from large suburban districts . Enrollments from town and rural locale codes reported pass rates close to the overall mean (81 .7%) with pass rates of 81 .8% and 79 .6%, respectively .

Table 2. GaVS Total Enrollment and Pass Rate by NCES Locale Code

Georgia Virtual School

Locale

2013-14 School Year

# of Enrolls % Pass

City, Large 43 93 .0%

City, Midsize 702 82 .2%

City, Small 915 73 .2%

Suburb, Large 9,927 83 .0%

Suburb, Midsize - -

Suburb, Small 36 88 .9%

Town, Fringe 120 89 .2%

Town, Distant 583 81 .1%

Town, Remote 79 75 .9%

Rural, Fringe 1,572 80 .1%

Rural, Distant 970 79 .7%

Rural, Remote 162 74 .1%

Out of State 53 86 .8%

Total 15,162 81.7%

12 Pass rate is calculated by dividing the number of completed enrollments who earned 70% of the course points by the total number of completed enrollments .



GaVS Subject Area and Locale DescriptivesWhile there was some variance among subject area enrollment by locale code, there were high enrollment subject areas across locale codes . Foreign language and literature was among at least the top three highest enrollment subject areas for all of the locale codes and represented the total highest enrollment for three of the four locales (city, town, rural) . Social sciences and history and life and physical sciences were also among the highest enrollment subject areas, placing in at least the top five for all four locale codes . While this consistency is present among high enrollment subject areas, the actual distribution of enrollments within these subject areas did vary among the locale codes .

Cities recorded enrollments in five subject areas (foreign language and literature – 424; social sciences and history – 333; mathematics – 256; life and physical sciences – 181; and English language and literature – 137), accounting for 80% of the total subject area enrollments for that locale . Suburbs recorded over 1,000 enrollments in five separate subject areas (social sciences and history – 1,910; physical, health, and safety education – 1,830; foreign language and literature – 1,580; mathematics – 1,544; and life and physical sciences – 1,253) again accounting for approximately 81% of the total subject area enrollments for that locale .

Towns recorded enrollments in three subject areas that individually exceeded 100 enrollments (foreign language and literature – 247; life and physical sciences – 114; social sciences and history – 113) . Combined, these accounted for approximately 60% of total subject area enrollments for that locale which, given the overall low enrollments for towns, was considerable .

Finally, rural locales recorded 41% of total enrollments in a single subject area (foreign language and literature – 1,122) and another 38% in three subject areas (social sciences and history – 475; life and physical sciences – 316; mathematics – 230) . No other locale had a single subject area account for that much of the total enrollments; this may suggest an unusual demand for foreign language and literature courses in rural areas, or more likely, a dearth of foreign language opportunities at local schools .



Table 3. GaVS Total Enrollment and Pass Rate by NCES Locale Code and SCED13 Subject Area


NCES SCED Subject Area

2013-14 School Year

City Suburb Town Rural Out of State

# of Enrolls % Pass # of

Enrolls % Pass # of Enrolls % Pass # of

Enrolls % Pass # of Enrolls % Pass

Agriculture, Food, and Natural Resources - - - - - - - - - -

Architecture and Construction - - - - - - - - - -

Business and Marketing 25 48 .0% 139 79 .1% 20 85 .0% 36 83 .3% - -

Communication and Audio/Visual Technology <10 100 .0% 27 77 .8% <10 0 .0% 12 91 .7% - -

Computer and Information Sciences 49 87 .8% 362 86 .7% 21 90 .5% 82 85 .4% - -

Engineering and Technology <10 33 .3% 39 82 .1% <10 100 .0% 9 88 .9% - -

English Language and Literature 137 72 .3% 766 74 .4% 85 68 .2% 161 74 .5% <10 100 .0%

Fine and Performing Arts 28 78 .6% 101 77 .2% 10 90 .0% 26 92 .3% 18 83 .3%

Foreign Language and Literature 424 81 .1% 1,580 82 .7% 247 81 .8% 1,122 79 .0% 11 81 .8%

Health Care Sciences <10 66 .7% 47 76 .6% <10 83 .3% 18 61 .1% - -

Hospitality and Tourism - - - - - - - - - -

Human Services - - - - - - - - - -

Life and Physical Sciences 181 75 .1% 1,253 76 .9% 114 78 .9% 316 76 .6% <10 87 .5%

Manufacturing - - - - - - - - - -

Mathematics 256 68 .4% 1,544 69 .7% 83 83 .1% 230 70 .9% <10 83 .3%

Military Science - - - - - - - - - -

Miscellaneous 102 85 .3% 245 83 .7% 29 82 .8% 71 85 .9% - -

Physical, Health, and Safety Education 84 89 .3% 1,830 97 .0% 43 93 .0% 79 86 .1% <10 100 .0%

Public, Protective, and Government Services 32 75 .0% 120 89 .2% <10 85 .7% 67 92 .5% - -

Religious Education and Theology - - - - - - - - - -

Social Sciences and History 333 79 .3% 1,910 87 .6% 113 86 .7% 475 83 .4% <10 100 .0%

Transportation, Distribution, and Logistics - - - - - - - - - -

Total 1,660 77.5% 9,963 83.0% 782 81.8% 2,704 79.6% 53 86.8%

GaVS Race, Ethnicity, and Locale DescriptivesPass rates by the race and ethnicity associated with enrollments were fairly stable across locale codes . In three of the four locales (city, suburb, and rural), enrollments identified as those from African American students were among the lowest pass rates, at approximately 67% for three of the four locales . Enrollments from Native Hawaiian or Pacific Islanders recorded 100% pass rates in three of the four locale codes (city, town, rural); this, however, should be interpreted with caution given the low enrollments: Native Hawaiian or Pacific Islanders accounted for less than 1% of the total enrollments in the 2013-14 school year . Asian students, who represented a larger percent of total enrollments (approximately 12%), recorded the highest or second highest pass rate in all four locales with overall pass rates ranging from 88 .1% (city) to 94 .8% (suburb) .


13 School Courses for the Exchange of Data .


Table 4. GaVS Pass Rate by NCES Locale Code and Student Race/Ethnicity


Race/Ethnicity

2013-14 School Year

Pass Rate

City Suburb Town Rural Out of State

American Indian or Alaska Native 88 .9% 92 .9% 84 .6% 66 .7% -

Asian 88 .1% 94 .8% 91 .3% 93 .0% 100 .0%

African American 67 .6% 66 .6% 81 .7% 67 .1% 75 .0%

Native Hawaiian or Pacific Islander 100 .0% 85 .7% 100 .0% 100 .0% -

White 82 .8% 86 .1% 81 .5% 80 .3% 85 .0%

Hispanic or Latino 74 .7% 71 .7% 75 .0% 73 .9% 100 .0%

Two or More Races 74 .2% 78 .3% 76 .2% 80 .2% 100 .0%

Blank 66 .7% 100 .0% - - -

Total 77.5% 83.0% 81.8% 79.6% 86.8%

GaVS Gender and Locale DescripitivesPass rate by gender was fairly stable across locale codes . In all but one locale (city — which reported the lowest total pass rate of any locale code) enrollments by female students averaged a pass rate above (in two cases well above) 80%, and in all locales (excluding out-of-state) males averaged a pass rate below 80% . This means that for all locales, females outperformed males with pass rates ranging from 3 .6% (city) to 7 .5% (suburb) higher . Interestingly, females also recorded higher overall enrollment totals (approximately 60% of the total enrollments) for each of the four locale codes; not only are more females taking online courses, but they are also outperforming their male counterparts statewide .

Table 5. GaVS Total Enrollment and Pass Rate by Gender and Locale Code


Locale

2013-14 School Year

Female Male

# of Enrolls % Pass # of Enrolls % Pass

City 978 79 .0% 682 75 .4%

Suburb 5,315 86 .5% 4,648 79 .0%

Town 471 84 .7% 311 77 .5%

Rural 1,523 81 .5% 1,181 77 .1%

Out of State 25 84 .0% 28 89 .3%

Total 8,312 84.6% 6,850 78.3%



About the Virtual High SchoolThe Virtual High School (VHS) is a nonprofit organization offering online courses to students around the world . Well known for its unique elective courses and extensive catalog of AP offerings, VHS offers online, blended, and customized programs for enrichment, core credit, and credit recovery . In 2014-15, VHS served over 18,000 enrollments from over 600 schools, in 40 U .S . states/territories, and 33 countries .

VHS Enrollment DescriptivesVHS serves diverse student populations across the country and internationally . As a result, there are many enrollments with blank locale codes, accounting for 7 .5% of the total enrollments . VHS had 12,040 completed course enrollments in the 2013-14 school year with an overall pass rate of 87 .2%14 and a completion rate of 81 .6% . VHS recorded enrollments in all but one of the locale codes identified by the NCES, with approximately 51% of enrollments (in the 2013-14 school year) coming from the suburb locale . The second largest enrollment group by locale was from the rural locale, accounting for approximately 23% of total enrollments in the 2013-14 school year . Pass rates across the locales were clustered very closely, ranging from 86% (city) to 89 .5% (town) .

Table 6. VHS Total Enrollment and Pass Rate by NCES Locale Code

The Virtual High School

Locale

2013-14 School Year

# of Enrolls % Pass

City 1,387 86 .0%

Suburb 6,173 87 .0%

Town 775 89 .5%

Rural 2,794 87 .9%

Blank 911 85 .5%

Total 12,040 87.2%

VHS Subject Area and Locale DescriptivesVHS recorded enrollments in 17 of the 22 SCED subject areas relatively consistently across the four locales . There was very little variance among high enrollment subject areas by locale code with the same three subject areas holding the first, second, and (in most cases) third highest enrollment spots in the four locale codes . Social sciences and history was the highest enrollment subject area, accounting for 24% of the total enrollments . Life and physical sciences was the second highest enrollment subject area across three of the four locales, accounting for 17% of the total enrollments overall; English language and literature accounted for 11% of the total enrollments . The three high enrollment subject areas (social sciences and history, life and physical sciences, and English language and literature) accounted for approximately half of all enrollments for each of the four locale codes (ranging from approximately 45% in cities to 57% of enrollments in towns) .

Of the four locales, only two remained either consistently in the top half or bottom half regarding overall pass rate and pass rate of the three high enrollment subject areas . Enrollments from towns recorded the highest overall pass rate (89 .5%) and the highest pass rate in English language and literature (89 .6%), as well as the second highest pass rates for social sciences and history (91 .8%) and life and physical sciences (90 .8%) . The suburb locale, on the other hand, recorded the second lowest pass rate overall (87%) and for all three of the high enrollment subject areas (social sciences and history – 88 .4%; life and physical sciences – 86 .6%; English language and literature – 83 .5%) .

14 Pass rate is calculated by dividing the number of enrollments that earned 60% of the course points by the total number of enrollments .



Enrollments from both the city and rural locales did not have consistent rankings across the three high enrollment subject areas . Enrollments from the city locale recorded the lowest overall pass rate (86%) and the lowest pass rate for life and physical sciences (85 .8%), yet recorded the highest pass rate and second highest pass rate for social sciences and history (92 .8%) and English language and literature (85 .2%) . Enrollments from the rural locale showed a trend similar to those from cities, recording the second highest overall pass rate (87 .9%) and highest pass rate for life and physical sciences (91 .7%), and the lowest overall pass rate for social sciences and history (87 .8%) and English language and literature (84 .6%) .

Table 7. VHS Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area



2013-14 School Year

City Suburb Town Rural Blank




Agriculture, Food, and Natural Resources 10 90 .0% 90 93 .3% <10 87 .5% 50 94 .0% <10 100 .0%


Business and Marketing 57 93 .0% 356 89 .3% 30 93 .3% 113 85 .0% 48 95 .8%

Communication and Audio/Visual Technology <10 100 .0% 43 86 .0% <10 100 .0% 13 84 .6% <10 100 .0%

Computer and Information Sciences 94 81 .9% 476 84 .7% 58 69 .0% 251 84 .5% 85 84 .7%

Engineering and Technology 22 81 .8% 107 85 .0% 10 100 .0% 56 85 .7% 12 83 .3%

English Language and Literature 189 85 .2% 601 83 .5% 125 89 .6% 350 84 .6% 111 84 .7%


Foreign Language and Literature 148 79 .1% 383 83 .6% 53 88 .7% 133 87 .2% 49 71 .4%

Health Care Sciences - - - - - - - - - -


Human Services <10 66 .7% 35 94 .3% <10 80 .0% 16 87 .5% - -

Life and Physical Sciences 219 85 .8% 1,108 86 .6% 119 90 .8% 509 91 .7% 148 82 .4%

Manufacturing - - - - - - - - - -

Mathematics 192 80 .2% 457 90 .6% 41 92 .7% 238 85 .3% 120 88 .3%


Miscellaneous 71 90 .1% 212 84 .9% 21 90 .5% 106 86 .8% 25 88 .0%

Physical, Health, and Safety Education 34 94 .1% 85 95 .3% 34 100 .0% 35 97 .1% <10 100 .0%

Public, Protective, and Government Services 59 91 .5% 299 89 .0% 32 81 .3% 122 88 .5% 27 96 .3%

Religious Education and Theology <10 100 .0% 21 76 .2% <10 100 .0% 13 92 .3% <10 100 .0%

Social Sciences and History 221 92 .8% 1,598 88 .4% 194 91 .8% 624 87 .8% 210 86 .7%

Transportation, Distribution, and Logistics <10 66 .7% 24 87 .5% <10 100 .0% 12 83 .3% <10 33 .3%

Total 1,387 86.0% 6,173 87.0% 775 89.5% 2,794 87.9% 911 85.5%



VHS Gender and Locale DescriptivesPass rate by gender was fairly stable across locale codes . In all locales, enrollments by female students averaged a pass rate in the high 80% to low 90%, and in all locales males averaged a percentage pass rate in the low to mid 80% . This means that for all locales, females outperformed males with pass rates ranging from 4 .1% (town) to 7 .1% (city) higher . Interestingly, females also recorded higher overall enrollment totals for each of the four locale codes (approximately 60% of the total enrollments, as high as 68% for towns); not only are more females taking online courses, but they are also outperforming their male counterparts .

Table 8. VHS Total Enrollment and Pass Rate by Gender and Locale Code


Locale

2013-14 School Year

Female Male


City 828 88 .9% 559 81 .8%

Suburb 3,678 88 .8% 2,495 84 .5%

Town 525 90 .9% 250 86 .8%

Rural 1739 90 .5% 1055 83 .8%

Blank 480 88 .3% 431 82 .4%

Total 7,250 89.3% 4,790 83.9%



About the Michigan Virtual SchoolThe Michigan Virtual School is a division of MVU, a 501(c)(3) nonprofit organization that works in partnership with K-12 schools to supplement and expand online learning opportunities primarily for students in grades 9 through 12 . For the past 16 years, MVU has provided leadership to accelerate the adoption and use of online learning within Michigan . MVS was created by Public Act 230 of 2000 to serve both traditional and nontraditional students and offers a broad range of core academic courses aligned with state standards, college-level equivalent courses, remedial, enrichment and world language courses, and other innovative online experiences .

MVS Enrollment DescriptivesAll data regarding student demographics (gender, race/ethnicity, locale, etc .) are entered at the time of enrollment by the enrolling entity (typically either a school or parent) . As a result, MVS demographic data is not uniformly provided, which sometimes leads to large gaps in data, including in the analysis done for this study wherein approximately 12% of enrollments had a blank locale code . These data aside, in the 2013-14 school year MVS had 20,142 completed course enrollments with an overall pass rate of 80 .1%15 and a completion rate of 97 .2% .16 MVS recorded enrollments in all of the locale codes identified by the NCES, with approximately 31% of enrollments coming from the suburb locale . The second largest enrollment group by locale was rural, accounting for approximately 26% of total enrollments . Pass rates across most of the locales were clustered very closely around low to mid 80%, ranging from 80 .1% (rural) to 84 .6% (town); the city locale recorded the lowest overall pass rate (64 .9%) .

Table 9. MVS Total Enrollment and Pass Rate by NCES Locale Code

Michigan Virtual School

Locale

2013-14 School Year

# of Enrolls % Pass

City 1,996 64 .9%

Suburb 6,167 81 .1%

Town 4,297 84 .6%

Rural 5,245 80 .1%

Blank 2,437 81 .9%

Total 20,142 80.1%

MVS Subject Area and Locale DescriptivesMVS recorded enrollments in 15 of the 22 SCED subject areas relatively consistently across the four locales . There was some variation, but for many subject areas each locale was proportionally represented . There was very little variance among high enrollment subject areas by locale code with the same four subject areas holding the first through fourth highest enrollment spots in the four locale codes . Foreign language and literature was the highest enrollment subject area across three of the four locales (and within 1% of the highest for the fourth), accounting for 21% of the total enrollments . Social sciences and history was the second highest enrollment subject area across three of the four locales (again, within 1% for the fourth locale), accounting for 16% of the total enrollments . The third highest enrollment was in mathematics, accounting for 15% of the total enrollments . Finally, life and physical science was the fourth highest enrollment subject area, accounting for 12% of the total enrollments . The four high enrollment subject areas (foreign language and literature, social sciences and history, mathematics, and life and physical science) represented over half of all enrollments (64% of the total enrollments) for each of the four locale

15 Pass rate is calculated by dividing the number of enrollments that earned 60% of the course points by the total number of enrollments . 16 http://media .mivu .org/pdf/Leg_Report_2014 .pdf


http://media.mivu.org/pdf/Leg_Report_2014.pdf


codes (ranging from approximately 56% in towns to 68% of enrollments in cities) . For the most part, of the four locale codes, only two remained either consistently in the top half (town) or bottom half (city) regarding overall pass rate and pass rate of the four high enrollment subject areas . Enrollments from towns recorded the highest overall pass rate (84 .6%) and the highest pass rate in foreign language and literature (86 .8%) and mathematics (80 .1%), as well as the second highest pass rates for social sciences and history (85 .7%) and life and physical sciences (84 .2%) .

The city locale, on the other hand, recorded both the lowest overall pass rate (64 .9%) and the lowest pass rate for all four of the high enrollment subject areas (foreign language and literature – 69 .6%; social sciences and history – 72 .8%; mathematics – 58 .2%; and life and physical sciences – 61 .5%) . Enrollments from both the suburb and rural locales did not have consistent ranking across the four high enrollment subject areas . Enrollments from the suburb locale recorded the highest overall pass rate for social sciences and history (87 .7%) and life and physical sciences (84 .7%) but the second lowest pass rate for both foreign language and literature (77 .3%) and mathematics (76 .8%) . Enrollments from the rural locale did not record either the highest or lowest overall pass rate for any of the four high enrollment subject areas .

Table 10. MVS Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area



2013-14 School Year





Agriculture, Food, and Natural Resources <10 100 .0% - - - - - - <10 100 .0%



Communication and Audio/Visual Technology <10 100 .0% 23 82 .6% 15 80 .0% 33 90 .9% 11 81 .8%

Computer and Information Sciences 37 81 .1% 293 73 .4% 198 77 .8% 206 80 .1% 35 82 .9%

Engineering and Technology <10 100 .0% 30 90 .0% 35 100 .0% 32 81 .3% <10 100 .0%

English Language and Literature 231 33 .8% 385 73 .8% 296 64 .5% 306 64 .4% 265 71 .3%


Foreign Language and Literature 392 69 .6% 1,090 77 .3% 963 86 .8% 1,373 82 .6% 458 85 .2%

Health Care Sciences 25 88 .0% 166 84 .9% 118 89 .0% 164 84 .1% 14 92 .9%


Human Services - - - - - - - - - -

Life and Physical Sciences 231 61 .5% 746 84 .7% 457 84 .2% 615 79 .0% 301 81 .7%

Manufacturing - - - - - - - - - -

Mathematics 318 58 .2% 816 76 .8% 477 80 .1% 679 78 .6% 629 77 .7%


Miscellaneous 118 77 .1% 460 84 .3% 273 93 .0% 293 80 .5% 50 86 .0%

Physical, Health, and Safety Education 54 68 .5% 296 86 .5% 85 71 .8% 127 69 .3% 145 88 .3%

Public, Protective, and Government Services 33 84 .8% 107 81 .3% 293 93 .2% 148 90 .5% 11 90 .9%


Social Sciences and History 405 72 .8% 1,069 87 .7% 517 85 .7% 833 81 .2% 437 88 .1%


Total 1,996 64.9% 6,167 81.1% 4,297 84.6% 5,245 80.1% 2,437 81.9%



MVS Gender and Locale DescriptivesPass rate by gender was fairly stable across locale codes . In all but one locale (city — which reported the lowest total pass rate of any locale code) enrollments by female students averaged a pass rate above 80%; in all but one locale, males averaged a pass rate below 80% . This means that for all locales, females outperformed males with pass rates ranging from 2 .4% (suburb) to a staggering 12 .3% (city) higher . Interestingly, females also recorded higher overall enrollment totals (approximately 57% of the total enrollments) for each of the four locale codes (not for enrollments with blank locales); not only are more females taking online courses with MVS, they are also outperforming their male counterparts statewide .

Table 11. MVS Total Enrollment and Pass Rate by Gender and Locale Code


Locale

2013-14 School Year

Female Male


City 1,103 70 .4% 893 58 .1%

Suburb 3,483 82 .1% 2,684 79 .7%

Town 2,475 86 .8% 1,822 81 .7%

Rural 3,108 82 .0% 2,137 77 .3%

Blank 1,195 84 .7% 1,242 79 .2%

Total 11,364 82.3% 8,778 77.2%

MVS Enrollment Reason and Locale DescriptivesUnsurprisingly, the pass rate was fairly consistent across locale code by enrollment reason . Also unsurprisingly, enrollments that identified their enrollment reason as credit recovery recorded the lowest pass rate (51 .8%), and those that identified a scheduling conflict recorded the highest pass rate (88 .1%) . Enrollments that identified taking an online course because it was the “preference of the student” also performed below the overall pass rate of 80 .1% .

Table 12. MVS Total Enrollment and Pass Rate by Enrollment Reason and Locale Code


Enrollment Reason

2013-14 School Year

Pass Rate

City Suburb Town Rural Blank Total

Course Unavailable at Local School 79 .5% 82 .2% 89 .9% 85 .7% 82 .7% 85.2%

Credit Recovery 26 .3% 68 .5% 52 .8% 53 .9% 70 .2% 51.8%

Learning Preference of the Student 76 .4% 67 .0% 79 .2% 68 .3% 84 .2% 73.5%

Scheduling Conflict 90 .6% 88 .1% 89 .1% 84 .4% 92 .1% 88.1%

Other 78 .3% 83 .2% 73 .8% 66 .8% 83 .8% 79.7%



About IDEAL-NMIDEAL-NM (Innovative Digital Education and Learning-New Mexico) is a statewide eLearning program of the New Mexico Public Education Department . IDEAL-NM offers statewide virtual courses in collaboration with New Mexico schools . Through the virtual school, districts and charter schools are able to expand educational opportunity for students . IDEAL-NM provides online courses taught by a licensed New Mexico teacher . The enrolling school provides students with the space and technology and designates a site coordinator or learning coach to monitor and support students to ensure success . All New Mexico schools have access to the statewide learning management system (LMS) that serves as the platform for the development and delivery of online and blended learning programs . IDEAL-NM provides the training, technical assistance, and help-desk support to partner schools in the development of quality online programs . Through the statewide LMS, schools have access to 116 semester-long IDEAL-NM courses for supplemental, blended, and fully online learning .

IDEAL-NM Enrollment DescriptivesIDEAL-NM had 1,637 completed course enrollments in the 2013-14 school year with an overall pass rate of 82%17 and a completion rate of 90% . IDEAL-NM considers a “passing enrollment” one that earns at least 60% of the available course points . IDEAL-NM serves students in all of the main locale codes identified by NCES, with approximately 50% of 2013-14 school year enrollments coming from the rural locale . Approximately 50% of all enrollments cite enrichments or the course not being offered locally as the reason for enrollment . As such, they serve high numbers of upper-level and AP courses to students in rural locales . The next largest enrollment group was from the city locale, accounting for approximately 23% of total enrollments in the 2013-14 school year, followed by the town locale, accounting for approximately 16% . It should be noted that 10% of the enrollments did not specify a locale code at enrollment .

Enrollments from the city locale recorded the lowest overall pass rate (68 .8%) while the suburb locale recorded the highest overall pass rate (93 .3%); however, it should be noted that the suburb locale accounted for only 1% of the total enrollments . The rural locale had the second highest pass rate and with the high number of enrollments, a more stable pass rate of 87 .7% . The remote rural locale had the highest overall individual pass rate of any sub-locale at 94 .8%; again with the high enrollment, this pass rate is more stable than the pass rate of 93 .3% for the suburban midsize locale .


Ideal-NM


Table 13. IDEAL-NM Total Enrollment and Pass Rate by NCES Locale Code

IDEAL-NM

Locale

2013-14 School Year

# of Enrolls % Pass

City, Large 87 72 .4%

City, Midsize - -


Suburb, Large - -

Suburb, Midsize 15 93 .3%

Suburb, Small - -

Town, Fringe - -

Town, Distant 41 90 .2%


Rural, Fringe 270 77 .0%



Blank 170 82 .4%

Total 1,637 82.0%

IDEAL-NM Subject Area and Locale DescriptivesWhile there was some variance among subject area enrollment by locale code, there were high enrollment (relative to overall locale enrollment) subject areas across locale codes, with three subject areas accounting for 64% of the total enrollments across all locales . Social sciences and history was among the high enrollment subject areas for three of the four locale codes (city, town, rural) . Mathematics (town, rural), physical, health, and safety education (city, rural), and foreign language and literature (suburb, rural) were among the high enrollment subject areas for two of the four locale codes . While there was this consistency among high enrollment subject areas, the actual distribution of enrollments within these subject areas did vary among the locale codes .

Cities recorded relatively high enrollments (over 50) in two subject areas (social sciences and history and physical, health, and safety education) accounting for 63% of the total subject area enrollments for that locale . Suburban districts only recorded enrollments in three subject areas: foreign language and literature, fine and performing arts, and social sciences and history . Of these three, only one (foreign language and literature) recorded more than 10 enrollments . Towns recorded relatively high enrollments (over 50) in two subject areas (mathematics, and social sciences and history), accounting for 52% of the total subject area enrollments for that locale . Finally, rural locales recorded over 100 enrollments in five subject areas (mathematics – 197; physical, health, and safety education – 180; social sciences and history – 136; foreign language and literature – 117; and English language and literature – 105) . These five subject areas accounted for 89% of the total enrollments for the rural locale .

Ideal-NM


Table 14. IDEAL-NM Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area

IDEAL-NM


2013-14 School Year







Business and Marketing <10 50 .0% - - <10 100 .0% <10 88 .9% 10 20 .0%

Communication and Audio/Visual Technology - - - - <10 100 .0% - - - -

Computer and Information Sciences <10 60 .0% - - <10 100 .0% 23 91 .3% <10 100 .0%

Engineering and Technology - - - - - - - - - -

English Language and Literature 25 76 .0% - - 34 64 .7% 105 88 .6% 16 50 .0%

Fine and Performing Arts <10 100 .0% <10 100 .0% <10 100 .0% <10 88 .9% 28 60 .7%

Foreign Language and Literature 16 87 .5% 12 91 .7% 25 100 .0% 117 82 .9% <10 100 .0%



Human Services - - - - - - - - - -

Life and Physical Sciences 16 43 .8% - - - - 29 79 .3% <10 87 .5%

Manufacturing - - - - - - - - - -

Mathematics 48 81 .3% - - 78 76 .9% 197 96 .4% 23 100 .0%


Miscellaneous 17 70 .6% - - <10 100 .0% 17 70 .6% <10 100 .0%

Physical, Health, and Safety Education 88 56 .8% - - 30 86 .7% 180 84 .4% 19 100 .0%

Public, Protective, and Government Services - - - - - - - - - -

Religious Education and Theology <10 100 .0% <10 100 .0% <10 100 .0% - -

Social Sciences and History 146 70 .3% <10 100 .0% 56 83 .9% 136 86 .0% 56 96 .4%


Total 370 68.8% 15 93.3% 258 81.8% 824 87.7% 170 82.4%

Ideal-NM


About North Carolina Virtual Public SchoolThe North Carolina Virtual Public School (NCVPS) was established in 2005 on the recommendation of the North Carolina e-Learning Commission . NCVPS’s purpose is to provide e-learning opportunities to students . The initial online course offerings in summer 2007 were for high school students . In subsequent years, course offerings were made available for middle school students . In school year 2007-08, NCVPS enrolled 17,325 students . Since then, NCVPS has had over 193,000 course enrollments . NCVPS is the second largest state virtual school in the United States18 and provides students online courses in many subject areas including mathematics, science, English language arts, social studies, arts, advanced placement, honors, and world languages . Other courses include test preparation, credit recovery, and Occupational Course of Study (OCS) .

NCVPS is a supplemental service to the public schools of North Carolina . Students enroll through their local public school, grades are reported to their public school, and their school awards credit . The courses use learning management and collaborative software to maximize student interaction in each class . NCVPS teachers use the latest technologies to engage students as well as prepare them to be career and college ready .

NCVPS Enrollment DescriptivesNCVPS had 32,926 completed course enrollments in the 2013-14 school year, with an overall pass rate of 83 .6%19 and a completion rate of 83 .1% . NCVPS recorded enrollments in all of the locale codes identified by NCES . Rural districts comprised the highest enrollment locale with 38% of enrollments . The sub-locale of fringe rural alone accounted for 24% of the total enrollments, with nearly twice as many enrollments as the next highest sub-locale (large suburb – 5,137) . The next largest enrollment group was from the city locale, with approximately 28% of total enrollments in the 2013-14 school year, followed by the suburb locale accounting for approximately 23%, and towns accounting for 10% .

Pass rates were closely clustered around the mid to low 80% range (80%-86 .6%) for all four locale codes . Enrollments from the city locale recorded the lowest overall pass rate (80%), and the rural locale recorded the highest overall pass rate (86 .6%) . The small suburb locale recorded a pass rate of 97 .6% . It should be noted, however, that the small suburb locale accounted for less than 1% of the total enrollments .

18 http://www .kpk12 .com/wp-content/uploads/Evergreen_KeepingPace_2015 .pdf19 Pass rate is calculated by dividing the number of enrollments that earned 70% of the course points by the total number of enrollments .

North Carolina Virtual Public School


Table 15. NCVPS Total Enrollment and Pass Rate by NCES Locale Code


Locale

2013-14 School Year

# of Enrolls % Pass

City, Large 4,525 78 .2%

City, Midsize 2,165 83 .2%

City, Small 2,585 83 .3%


Suburb, Midsize 2,347 87 .9%


Town, Fringe 1,005 82 .2%

Town, Distant 2,292 81 .0%



Rural, Distant 3,420 85 .7%


Blank 297 89 .6%

Total 32,926 83.6%

NCVPS Subject Area and Locale DescriptivesNCVPS recorded relatively consistent enrollments across the four locales in 11 of the 22 SCED subject areas . There was very little variance among high enrollment subject areas by locale code with the same four subject areas holding the first, second, third, and fourth highest enrollment spots in all four locale codes . Foreign language and literature was the highest enrollment subject area, accounting for 29% of the total enrollments . Social sciences and history was the second highest enrollment across all four locales with 22% of the total enrollments . Miscellaneous course enrollments and those in life and physical sciences comprised the third and fourth highest enrollment subject areas, respectively . In all four of the high enrollment subject areas, enrollments from the rural locale earned the highest pass rate (ranging from 83 .9% to 93%), excluding blank enrollments .



Table 16. NCVPS Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area



2013-14 School Year








Communication and Audio/Visual Technology 66 77 .3% 72 83 .3% 24 70 .8% 157 88 .5% 10 100 .0%

Computer and Information Sciences 165 89 .1% 207 81 .6% 35 74 .3% 196 79 .1% <10 100 .0%




Foreign Language and Literature 2,402 75 .1% 1,896 83 .9% 927 79 .2% 4,160 83 .9% 69 84 .1%



Human Services - - - - - - - - - -

Life and Physical Sciences 1,133 89 .1% 705 87 .2% 404 88 .6% 1,081 93 .0% 34 85 .3%

Manufacturing - - - - - - - - - -

Mathematics 849 76 .9% 781 76 .7% 197 69 .5% 697 81 .6% <10 100 .0%


Miscellaneous 1,145 76 .0% 1,049 75 .8% 411 78 .3% 1,686 85 .5% 34 94 .1%

Physical, Health, and Safety Education 214 86 .4% 41 85 .4% 26 88 .5% 35 94 .3%



Social Sciences and History 2,032 87 .0% 1,566 88 .6% 894 86 .4% 2,527 90 .6% 70 87 .1%


Total 9,275 80.8% 7,568 83.0% 3,394 81.6% 12,392 86.6% 297 89.6%



About VirtualSC VirtualSC (Virtual South Carolina) is a state-sponsored online program serving students currently attending public, private, and home schools in grades 7-12 and Adult Education Programs . VirtualSC offers free, rigorous online courses aligned to state standards that are developed and taught by highly qualified, South Carolina-licensed teachers . VirtualSC partners with schools to provide a free individualized online learning solution for students on the path to high school graduation .

VirtualSC Enrollment DescriptivesVirtualSC had 15,098 completed course enrollments in the 2013-14 school year with an overall pass rate of 90 .1%20 and a completion rate of 74 .3% . VirtualSC considers a “passing enrollment” as one that earns at least 70% of the available course points . VirtualSC serves students in all but one (large city) of the locale codes identified by NCES, with approximately 37% of enrollments in the 2013-14 school year coming from large suburban schools . The data from VirtualSC is unique in that it identifies the locale at the school level as opposed to the other virtual schools included in the report whose data comes from the district level . No tests were run to determine statistical significance; however, enrollments from remote town districts recorded the lowest overall pass rate (85 .8%) . Rural districts overall (fringe, distant, and remote combined) recorded the lowest pass rate of the four locale codes at 89 .1% . Enrollments from suburban and city schools recorded the highest pass rates — 90 .7% and 90 .6%, respectively . Enrollments from town schools recorded an overall pass rate of 89 .8% . Remote rural enrollments reached the highest overall pass rate at 94 .7% . It should be noted that remote rural accounted for a very small percentage of the total enrollment with less than 20 enrollments .

Table 17. VirtualSC Total Enrollment and Pass Rate by NCES Locale Code

VirtualSC

Locale

2013-14 School Year

# of Enrolls % Pass

City, Large - -

City, Midsize 602 92 .2%

City, Small 2,144 90 .2%








Rural, Distant 1,212 87 .6%


Total 15,098 90.1%

VirtualSC Subject Area and Locale DescriptivesVirtualSC had enrollments in 15 of the 22 SCED identified subject areas in the 2013-14 school year . While there was some minor variation among subject area enrollments by locale code, there were similar high enrollment subject areas across locales . Social sciences and history was the highest enrollment subject area for both suburb and rural locales, also placing third for town and city locales . Mathematics was the highest enrollment subject area


VirtualSC


for schools in the city and town locales, and the third highest for the rural and suburb locales . English language and literature and physical, health, and safety education were the final two high enrollment subject areas that were mostly consistent across locales . No single subject area accounted for a uniquely high percentage of total enrollment; rather, each locale had a handful of high enrollment subject areas (relative to their total enrollment) that declined gradually .

Enrollments from the town locale recorded relatively consistently lower pass rates for the high enrollment subject areas (social sciences and history, mathematics, English language and literature, and physical, health, and safety education) placing fourth in three of the four subject areas (social sciences and history, computer and information sciences, and physical, health, and safety education) and first in one of the high enrollment subject areas (mathematics) . In contrast, enrollments from the city locale recorded consistently high pass rates in the high enrollment subject areas, placing first in social sciences and history and physical, health, and safety education, and second (with less than 1% difference between first and second place) in English language and literature . Schools in the city locale, however, also recorded the lowest overall pass rate for mathematics .

Table 18. VirtualSC Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area

VirtualSC


2013-14 School Year

City Suburb Town Rural



Enrolls % Pass

Agriculture, Food, and Natural Resources - - - - - - - -

Architecture and Construction <10 50 .0% <10 28 .6% - - <10 75 .0%

Business and Marketing 118 93 .2% 255 92 .9% 49 100 .0% 193 95 .3%

Communication and Audio/Visual Technology 79 89 .9% 172 93 .0% 12 83 .3% 94 88 .3%

Computer and Information Sciences 161 90 .7% 422 88 .4% 68 85 .3% 297 92 .3%

Engineering and Technology - - - - - - - -

English Language and Literature 377 95 .0% 1,083 95 .8% 250 93 .2% 756 94 .8%

Fine and Performing Arts 117 90 .6% 243 95 .1% 58 87 .9% 244 92 .2%

Foreign Language and Literature 59 88 .1% 135 88 .9% 70 92 .9% 193 70 .5%

Health Care Sciences 80 85 .0% 169 77 .5% 33 75 .8% 129 77 .5%

Hospitality and Tourism - - - - - - - -

Human Services 20 75 .0% 118 79 .7% 30 73 .3% 61 80 .3%

Life and Physical Sciences 190 94 .2% 545 92 .7% 86 91 .9% 343 91 .8%

Manufacturing - - - - - - - -

Mathematics 545 85 .5% 1,056 88 .6% 357 90 .8% 740 86 .8%

Military Science - - - - - - - -

Miscellaneous 55 76 .4% 179 73 .7% 44 79 .5% 88 69 .3%

Physical, Health, and Safety Education 497 95 .8% 706 94 .6% 92 94 .6% 627 94 .3%

Public, Protective, and Government Services <30 73 .9% <50 83 .7% 13 100 .0% <30 75 .9%

Religious Education and Theology - - - - - - - -

Social Sciences and History 423 90 .1% 1,168 89 .9% 204 86 .3% 887 87 .1%

Transportation, Distribution, and Logistics - - - - - - - -

Total 2,746 90.6% 6,301 90.7% 1,366 89.8% 4,685 89.1%

VirtualSC


VirtualSC Race, Ethnicity, and Locale DescriptivesPass rate by the race and ethnicity associated with enrollments were fairly stable across locale codes . In two of the four locale codes, enrollments from Asian students recorded the highest pass rate (city – 98 .1%; rural – 100%), while in the other two locale codes, enrollments from Asian students recorded the second highest pass rate (suburb – 95 .6%) and tied for fourth highest in the town locale . A similar pattern was seen for white non-Hispanic students . Conversely, enrollments from African American students recorded the lowest pass rate in three of the four locales (suburb – 86 .6%; town, 85 .8%; rural – 83 .1%), and the second lowest in the remaining locale (city – 85 .5%) .

Table 19. VirtualSC Pass Rate by NCES Locale Code and Student Race/Ethnicity

Virtual SC

Race/Ethnicity

2013-14 School Year

Pass Rate

City Suburb Town Rural

Asian 98 .1% 95 .6% 90 .0% 100 .0%

African American 85 .5% 86 .6% 85 .8% 83 .1%

Hispanic 91 .0% 91 .6% 89 .1% 92 .2%

Native American 84 .6% 97 .4% 90 .9% 100 .0%

Multi-Ethnic 94 .3% 88 .6% 93 .9% 85 .8%

White Non-Hispanic 93 .9% 92 .6% 93 .1% 91 .9%

Not Listed 93 .6% 88 .0% 90 .0% 89 .5%

Total 91.0% 90.6% 88.9% 87.3%

VirtualSC Gender and Locale DescriptivesPass rates by gender for each locale were fairly stable; in two of the four locales, enrollments from male students recorded a higher pass rate than enrollments from female students by a very narrow margin (differences range from 0 .3% to 1 .3%) . In the other two locales, enrollments from females narrowly outperformed males (differences range from 0 .1% to 0 .4%) . Given that there was less than 1% difference between the pass rate of enrollments from male and female students for three of the four locales and only 1 .3% for the fourth, it is reasonable to conclude similar performance across gender . The pass rates for each gender also followed the overall pass rates for the locales, with enrollments from the rural locale recording the lowest pass rate and those from the city and suburb recording the highest . Across all locale codes, females recorded higher enrollments, with nearly twice as many enrollments as male students in the city locale and approximately one and a half times as many in the rural .

Table 20. Virtual SC Total Enrollment and Pass Rate by Gender and Locale Code

VirtualSC

Locale

2013-14 School Year

Female Male


Enrolls % Pass

City 1,739 90 .5% 1,007 90 .8%

Suburb 3,656 90 .1% 2,645 91 .4%

Town 824 89 .9% 542 89 .8%

Rural 2,790 89 .5% 1,895 89 .1%

Total 9,009 90.0% 6,089 90.3%

VirtualSC


About Wisconsin Virtual School Wisconsin Virtual School (WVS) is a supplemental online course provider that partners with school districts throughout Wisconsin to offer online courses to middle and high school students . WVS has been operating out of the Cooperative Educational Service Agency #9 (CESA 9) since 2000 and has served over 27,000 enrollments to date, with over half of Wisconsin’s school districts participating in the program . The Wisconsin Department of Public Instruction has an agreement with WVS to provide online courses and services to Wisconsin school districts as a partner in the Wisconsin Digital Learning Collaborative21 (formerly the statewide Web Academy) .

WVS Enrollment DescriptivesWVS had 3,969 completed course enrollments in the 2013-14 school year with an overall pass rate of 98 .8%22 and a completion rate of 83% . Like many other virtual schools included in this report, WVS does not award credit for completed enrollments — credit is awarded locally . However, WVS reports the percentage of course points earned plus a calculation of the percentage of the course the student completed . The local district awards credit based on a required threshold for completion and the available course points earned .

In the 2013-14 school year, WVS recorded enrollments in all of the locale codes identified by NCES, with a vast majority of enrollments (79 .4%) coming from town and rural locales . The largest enrollment group by locale was from the rural locale, accounting for approximately 41 .2% of total enrollments, followed very closely by town districts accounting for approximately 38 .3% . City and suburb locales accounted for 8 .5% and 10 .3% of enrollments, respectively . WVS had consistently high pass rates (well over 90%) as a result of how WVS calculates “completed” course enrollments . Again, WVS does not make a determination of “passing grade;” that is left up to the local districts . For the purposes of this report, and to remain consistent with other virtual school cases, only completed enrollments are included in calculations . Students who complete 90% or more of the total course work receive a calculated average grade . Average grades are not calculated for students who complete less than 90% of the course . This means that even if the student does not drop or withdraw from the course, if they do not complete 90% of the course, they are not assigned a numerical grade . Because of this, the pass rate for WVS is somewhat artificially increased and should be interpreted with this in mind .

Four sub-locales (remote town, midsize suburb, midsize city, large city) had pass rates of 100%, and no single locale had a pass rate below 95% . The lowest individual pass rate for a locale was 97% in distant rural and small suburb locales . Given the extremely high and close pass rate for all of the locales, comparisons across locales are difficult .

21 http://dpi .wi .gov/imt/digital-learning/collaborative 22 Pass rate is calculated by dividing the number of enrollments that earned 60% of the course points by the total number of enrollments .

Wisconsin Virtual School

https://owa.mivu.org/owa/redir.aspx?SURL=WDzgCWE9pLwzbjY5bZ7xqdFlUfclOlE7Kk8sdGe_L49QhAbiOD3TCGgAdAB0AHAAOgAvAC8AZABwAGkALgB3AGkALgBnAG8AdgAvAGkAbQB0AC8AZABpAGcAaQB0AGEAbAAtAGwAZQBhAHIAbgBpAG4AZwAvAGMAbwBsAGwAYQBiAG8AcgBhAHQAaQB2AGUA&URL=http%3a%2f%2fdpi.wi.gov%2fimt%2fdigital-learning%2fcollaborative


Table 21. WVS Total Enrollment and Pass Rate by NCES Locale Code


Locale

2013-14 School Year

# of Enrolls % Pass

City, Large <10 100 .0%

City, Midsize <40 100 .0%


Suburb, Large 103 98 .1%






Rural, Fringe 741 98 .9%



Blank 67 100 .0%

Total 3,969 98.8%

WVS Subject Area and Locale DescriptivesWVS recorded enrollments in nine of the 22 SCED subject areas, again with consistently high pass rates above 90% . There was little variance among subject area enrollment by locale code as there were high enrollment subject areas across locale codes (relative to overall locale enrollment) with three subject areas (foreign language and literature, social sciences and history, and English language and literature) accounting for more than 50% of the total enrollments across all locales . Foreign language and literature was the highest enrollment subject area for three of the four locales (city, suburb, and rural), and second highest in the remaining locale (town) .

Social sciences and history, foreign language and literature, English language and literature, and mathematics were the four highest enrollment subject areas for all four locales accounting for 68% of the total enrollments . Foreign language and literature was the highest enrollment subject area for three of the four locales (city, suburb, rural) . While this consistency existed among high enrollment subject areas, the actual distribution of enrollments within these subject areas did vary among the locale codes .



Table 22. WVS Total Enrollment and Pass Rate by NCES Locale Code and SCED Subject Area



2013-14 School Year







Business and Marketing <10 100 .0% 30 100 .0% 67 98 .5% 126 100 .0% <10 100 .0%

Communication and Audio/Visual Technology - - - - - - - - - -

Computer and Information Sciences 16 100 .0% 25 100 .0% 97 100 .0% 121 97 .5% <10 100 .0%



Fine and Performing Arts 10 100 .0% <10 100 .0% 29 100 .0% 17 100 .0% - -

Foreign Language and Literature 90 100 .0% 111 97 .3% 240 99 .6% 343 99 .4% <10 100 .0%



Human Services - - - - - - - - - -

Life and Physical Sciences 33 93 .9% 25 95 .7% 146 99 .3% 137 97 .1% 11 100 .0%

Manufacturing - - - - - - - - - -

Mathematics 36 100 .0% 53 100 .0% 186 100 .0% 205 97 .1% 22 100 .0%


Miscellaneous - - - - - - - - - -

Physical, Health, and Safety Education 23 100 .0% 32 96 .9% 182 98 .9% 137 99 .3% <10 100 .0%



Social Sciences and History 76 100 .0% 87 97 .7% 345 99 .7% 304 98 .0% 10 100 .0%


Total 339 99.1% 409 97.8% 1,519 99.2% 1,635 98.5% 67 100.0%



Serving Rural StudentsWhile there are challenges and successes unique to each locale worthy of deeper investigation, our VSLA partners were most interested initially in understanding rural student populations, how they are served, and how well they perform compared to other locale groups . Thus we present two analyses below: a look at how each of the locales performs by virtual school, and a look at how (if at all) locale affects the disparity in pass rate by gender identified in many of the case studies .

Before we move on, it is important to understand how each virtual school is serving rural student populations in their respective states . While many factors go into why a district or student chooses to take an online course, given the constraints on rural districts and the opportunities afforded by online learning, particularly through state virtual schools, we might expect state virtual schools to over-serve rural students . That is to say that we may expect the percentage of rural students in state virtual schools to exceed the percentage of rural students statewide . This is the case for five of the seven state virtual schools included in this report . In some cases (New Mexico and Wisconsin) the percentage of rural students in the virtual school far exceeds the percentage of rural students statewide . While the NCES locale codes are at the district level, and therefore may erroneously include schools that are not actually in rural locales, we are able to get a rough estimate of how many students are in rural locales . In the case of New Mexico in particular, the percent of rural students in IDEAL-NM is more than double the percent of rural students statewide . In this case in particular it is reasonable to conclude that IDEAL-NM over-serves rural student populations .

Table 23. Distribution of Rural Districts and Students23 24 25

State

Statewide (2013-14)

Virtual School (2013-14)

% Rural Districts % Rural Students % Rural Students

Georgia 52 .0% 30 .0% 18 .0%

Massachusetts26 23 .0% 10 .0% 23 .0%

Michigan 44 .0% 21 .0% 25 .0%

New Mexico 60 .0% 20 .0% 50 .0%

North Carolina 50 .0% 46 .0% 38 .0%

South Carolina 42 .0% 29 .0% 31 .0%

Wisconsin 56 .0% 23 .0% 39 .0%

Effect of Locale on Successful Course CompletionAs is evident from the descriptive statistics presented in each of the cases and summarized in Figure 1, which orders locales from least probability of enrollments passing their online course (far left) to enrollments most likely to pass their online course (far right), there were performance differences across locales . While descriptive statistics can illuminate differences across groups, they are unable to speak to the magnitude of the difference or the significance . Our team wanted to find out if the differences evident in the descriptive analyses in the case studies were actually significant . To do that, we first explored the performance variable — the course completion status variable (pass/fail/incomplete) and/or the dichotomous course completion variable (pass or fail) — using chi-squared test to see whether there is any group difference in pass rate by locale code . The analysis found significant group differences in the course completion by locale code from five of the seven datasets .

23 https://nces .ed .gov/ccd/TableDisplay .asp?TablePath=tables/table_01 .asp24 https://nces .ed .gov/ccd/TableDisplay .asp?TablePath=tables/table_03 .asp25 Percentages for the virtual schools refer to students, not enrollments . Distinct counts of student identification codes were used to

calculate the percent of rural students in each virtual school . 26 The Virtual High School operates out of Massachusetts .

Discussion


Figure 1. Locale Order by Probability of Passing Online Course

GaVS City Rural Town Suburb

VHS City Rural Suburb Town

MVS City Rural Suburb Town

IDEAL-NM City Town Rural Suburb

NCVPS City Town Suburb Rural

VirtualSC Rural Town Suburb City

WVS Suburb Rural City Town

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As the chi-squared statistic does not give any information about the strength of the relationship or detailed contrasts between groups, the regression analysis was used to obtain this information . With the binary dependent variable (pass or fail), we performed logistic regression analysis to determine if differences in the likelihood of passing a course can distinguish between two paired groups identified by locale code and gender (e .g ., city vs . rural) . Because our team was not necessarily interested in the absolute difference in completion between locales but rather a comparison between them, the analysis was conducted based on comparing the likelihood of passing after plotting each locale's odds ratio against the reference group's . In the same vein, the team was more interested in observing statistics regarding the overall effect of the locale code variable and constraints of each pair of locales, not the logistic regression coefficient per se (i .e ., the change in the degree of probability for a shift in the condition) . Overall, the order of passing probability based on the estimated odds ratio corresponded to the descriptive results . Datasets included in our statistical analyses (GaVS, VHS, VirtualSC, and MVS) also include gender, which descriptive analyses suggest may influence the pass rate; thus, the results discussed below regarding locale control for the gender effect .

Discussion


For GaVS, enrollments in the suburb locale are more likely to pass the course than those in city, town, or rural locales . The likelihood of passing the course does not statistically differ between students in rural and city locales . The hypothesis test found a significant effect of locale code on the likelihood of passing the course (χ2(3) = 43 .73, p = 0) and a significant difference between students in rural and suburb areas (χ2(1) = 18 .94, p = 0) with students in the suburb areas outperforming those in rural areas . These results suggest that the highest performing locale — suburb — is distinctly different than the lower performing three locales in that students there are significantly more likely to pass their online courses .

We cannot contend that there is any statistical difference in the likelihood of successful course completion between city and suburb locales for IDEAL-NM . By contrast, we found statistical evidence in the differences between the town and city locales and between the rural and city locales . An additional hypothesis test confirms the overall effect of the locale code variable (χ2(3) = 88 .77, p = 0), and also students in the rural area outperform those in the town area in IDEAL-NM (χ2(1) = 11 .94, p<0 .001) .

For MVS, enrollments from three locales — suburb, town, and rural — indicated significant outperformance in comparison to those from the city locale . As the group with the highest pass rate, town is also significantly different from rural (χ2(1) = 32 .77, p = 0) or suburb areas (χ2(1) = 25 .36, p = 0) . The two locale groups of rural and suburb turned out to be statistically equivalent . Overall, the locale code variable was a significant factor in predicting MVS students’ performance in their online courses (χ2(3) = 278 .2, p = 0), with students in the city locale least likely to pass their online courses, and those in towns most likely to pass their online courses .

For NCVPS, the rural locale shows the most positive performance in terms of successful course completion . This outperformance is significantly different from city (the overall effect of locale code variable: χ2(3) = 144 .24, p = 0), suburb (test of rural vs . suburb: χ2(1) = 47 .77, p = 0), and town (test of rural vs . town: χ2(1) = 53 .19, p = 0) . The results found no statistical difference between city and town groups in the probability of passing the course . Results suggest that students in the rural locale are significantly more likely to pass their online courses than students in the other three locales, which are largely equivalent in terms of passing enrollments .

The variable of locale code has no power in prediction of the likelihood of successful course completion (χ2(3) = 5 .46, p = 0 .14) for VHS . Accordingly, comparing the other three locale codes to city reveals a significant group difference with town and no difference with suburb or rural . There is also no difference found between town and rural . Therefore, the locales of city, suburb, and rural have much in common when it comes to the likelihood of successful course completion .

For the VirtualSC dataset, students in rural areas are likely to underperform their counterparts in city areas, and the overall impact of locale code is marginally significant (χ2(3) = 8 .14, p = 0 .04) . There is no statistical difference between any other pairs of locale codes in the likelihood of passing the course . The overall impact of locale code seems to be exerted on the significant difference between rural versus city locales . Additionally, specific contrasts revealed a significant difference between rural and suburb (χ2(1) = 7 .01, p = 0 .0081) . Results suggest that students in rural locales are the least likely to pass their online courses compared to the other three locales, which are largely equivalent with some advantage for students .

For WVS, the variable of locale code has no power in predicting the likelihood of successful course completion (χ2(3) = 6 .6, p = 0 .08); functionally, students from all four locales were equally as likely to pass their online courses .

Discussion


Gender and Locale InteractionPrevious descriptive analyses in many of the virtual school case studies suggested that gender and locale code were important factors in student performance and pass rate . We performed statistical analyses to better understand the nature of the relationship between each variable and pass rate, as well as to understand the strength of the relationship . In some cases, gender or locale were significantly associated with the probability of a student passing a course, whereas in others the strength of relationship for gender differed across locales . For example, through logistic regression, we found that for GaVS both gender and locale were significantly associated with the probability of a student passing a course . Given this conclusion, we examined interaction effects between gender and locale by testing if the relationship between gender and passing a course differs as a function of locale .

Of the seven initial datasets, only four (GaVS, VHS, MVS, and VirtualSC) contained both variables of locale code as well as gender, and thus were analyzed for this section . From respective simple model results with only one variable, there was a gender difference in favor of females for three schools — GaVS, VHS, and MVS . VirtualSC had equivalent passing rates regardless of gender . With regard to simple models for locale, some of the locale differences in the course passing probability were significant for all four schools .

When including both variables in the model but not interaction terms, this multiple logistic regression model reaffirmed the main effects of gender as well as some of locales while controlling for effects of the other variable . Finally, interaction terms were added to the model, and this led to changes in the predictive power of gender to a non-significant level for GaVS (z statistics = -10 .28***26 without interaction terms and -1 .76 with interaction terms), which suggests some significant interactive function by gender and locale code variables . In other words, when looking at both gender and locale and their interactions, female students were no longer uniformly more likely to pass their online course .

Individual profiles on gender and locale for GaVS, MVS, VHS, and VirtualSC are presented below . For each profile, various coding schemes were used, including simple dummy coding with city and rural as reference groups, respectively, and finally, effect coding . Results based on dummy coding allow the comparison of each locale (i .e ., suburb, town, rural) to a reference level (i .e ., city or rural) while effect coding has some advantage in easily interpreting simple main effects without referring to a reference group . As previously mentioned, there was some significant interactive function by gender and locale, so we further examined the simple main effect of gender for each of the locale codes in comparison to the city and rural locale .

For GaVS, there was a significant gender difference for the suburb locale in comparison to the city: it was found that the gender difference for GaVS students residing in the suburb locale significantly deepened in comparison with their counterparts in the city (z statistics = 2 .49*27) . Thus, the pass rate disparity between male and female students (with female outperforming male) was most profound for students in the suburb locale, particularly when compared to the pass rate disparity for other locales . For example, when we compared pass rate by gender for the suburb locale to the rural locale, we found that the gender disparity in pass rate was not as profound for the rural locale as it was for the suburb locale (z statistics for suburb vs . rural = 2 .43*) . Finally, results based on effect coding synthesize this interactive function of gender and locale, in that the gender disparity for suburb was significantly different from the average of gender differences across the locale (χ2(1) = 4 .99*) . Given these results, we conclude that locale code is a key factor in determining the probability of passing a course — not only for individual students but for collective gender groups as well .

26 Three asterisks indicates the statistical significance at p < 0 .001 .26 One asterisk indicates the statistical significance at p < 0 .05 .

Discussion


We presumed that, unlike GaVS, the interactive function of the locale code variable on gender pass rate disparity for VHS would not be significant given that adding interaction terms into the model did not lead to an apparent change in the statistical status of gender main effect (z statistics = -8 .02*** without interaction terms and -3 .73*** with interaction terms) . Our results for each locale code corroborated this view . Descriptively, all locales indicated the gender disparity to some degree (% point difference for city = 7 .14; suburb = 4 .28; town = 4 .06; and rural = 6 .66) in favor of female students . Statistically the four locale groups, however, turned out to be equivalent in terms of the gender disparity . The simple main effect with each combination of gender and locale code was not statistically significant, indicating that there is no difference in terms of gender pass rate disparity in any of the three locales compared to the city, rural, or the average of all locales . The significant gender disparity is fairly stable across all locale areas .

With the MVS data, adding interaction terms to the model did not change the statistical significance itself for main effects of two variables . However, we found significant interactions by rural (z statistics = 2 .23*) and suburb (z statistics = 3 .16**28) in comparison with city . That is, the gender disparity in the city locale is much more profound than the disparity in rural or suburb areas . We also found that city and town are statistically equivalent as the profound gender disparity group (gender difference town vs . city: z statistics = 0 .9 with non-significance) whereas suburb and rural are equivalent as the less deep gender disparity group (gender difference suburb vs . rural: z statistics = 1 .06 with non-significance) . According to effect coding results, which allow for comparisons to the average gender difference across the locale, both of the widest gender disparities pertaining to city locale (female vs . mean by city vs . mean: coefficient = +0 .09, χ2(1) = 5 .64*) and the relatively reduced suburb locale (female vs . mean by suburb vs . mean: coefficient = -0 .09, χ2(1) = 8 .65**) are statistically significant .

VirtualSC has a uniquely different profile from the other virtual schools . In all of the models, with or without locale code, gender was not a significant predictor of a student passing an online course . Therefore, we conclude that there is no statistical evidence concerning the interactive effect of gender and locale variables on the probability of VirtualSC students passing their courses . Across all locales, female and male students had equal probability of passing their online courses .

Discussion

28 Two asterisks indicate the statistical significance at p<0 .01 .


We can conclude from the descriptive statistics presented in each of the case studies and from the statistical analyses of locale and gender and locale, that there is no common performance scenario that spans across all virtual schools . That is to say that each virtual school, its diverse geographic context, and the students it serves all interact to produce unique profiles of student success . There certainly were some similarities that are worth noting; for example, the city locale was descriptively the lowest performing locale for five of the seven virtual schools . Conversely, there was little consistency among the highest performing locale, with suburb, town, and rural filling each of the top three spots fairly evenly . The rural locale (along with suburb) was ranked everywhere from lowest to highest performing across the seven virtual schools, speaking again to the contextual and geographic variance of each virtual school .

When we looked closely at the interaction between gender and locale, we found variance in the specifics of performance by locale; we also found that the four virtual schools fell into one of two scenarios when gender was added to the model . For VHS and VirtualSC, adding gender to locale had little to no effect on the likelihood of passing one’s online course . That is not to say there were no gender differences . Rather, any disparity in pass rate by gender was consistent across locales . Alternatively, for GaVS and MVS adding gender to locale in the model deepened existing differences between female and male students across locales . For GaVS, the most profound difference in pass rate was for the suburb locale, with females significantly outperforming males . For MVS the most profound difference in pass rate was for the city locale, with female students significantly outperforming males . Understanding how gender interacts with locale in terms of pass rate provides valuable information for how to best serve students in all locales . It also provides a more robust picture of student performance . For example, in most virtual schools females outperformed males; but given how close the pass rates were for male and female students (in some cases within 1%-2%) in many virtual schools, there is no cause for concern or intervention . The profound gender pass rate disparities in some locales provide an opportunity for virtual schools to work closely with local schools and districts to understand better the students taking online courses, their motivation for taking a certain course online, and specific strategies to support them .

This report initially began with a desire to investigate rural districts and students served by state virtual schools . Through the work with our VSLA partners, we were able to build descriptive profiles of not only rural districts, but other locales as well . In doing so our work became less about focusing on one specific locale and, instead, trying to understand more deeply both what was happening inside each of the virtual schools as well as across them . Through our work, it became apparent that quantitative analyses could only tell part of the story of educating students across locales and that each virtual school had a different story . We identified a couple of overarching themes in the data but none that really spoke directly and specifically to rural students . This may be because our analyses focused at a very high level, taking into account pass rate and enrollment numbers and looking only at SCED subject areas (in contrast to something like AP or advanced level courses) . We view this report as laying the groundwork for understanding how virtual schools are serving students in each locale — a first step, that will lead us to asking questions tailored specifically to each virtual school and their unique context and needs, questions generated by findings such as the following:

1 . Students in the suburb locale at GaVS significantly outperform students in other locales . Does this match with statewide trends for face-to-face schools? Or are there additional supports/advantages that students in the suburbs have access to that contribute to their success online?

2 . Rural students at IDEAL-NM and NCVPS outperform other locales when taking into account all enrollments in all subject areas . Would this trend also hold true when constrained to only high-level or AP courses?

3 . Locale was not a factor in determining successful course completion at VHS and WVS . What are the main factors that contribute to successful course completion at these virtual schools?

4 . Students in the rural locale at VirtualSC were least likely to pass their online courses . Is there something unique to this locale or student population, and if so, what can VirtualSC do to better support these students?

Conclusion

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