Credit recovery is the fastest growing area of online learning (McCabe & St. Andrie, 2012). These programs are highly unregulated and there is minimal information on their enrollments or effectiveness. It would be fair to say that most credit recovery programs have not been examined empirically. For virtual learning providers, this segment has proven to be a gold mine. Apex Learning, estimates that 50% of its enrollments are for credit recovery. Aventa Learning reported a 500% increase in its credit recovery business. The Sloan Consortium stated that credit recovery is the most popular type of its fully online courses. Molnar (2013) predicted that revenues from the K-12 online learning industry would grow by 43% between 2010 and 2015, with revenues reaching $24.4 billion. As an educational researcher, this begs a question: Are virtual school/credit recovery operators preying on low-income students?
I assume that a disproportionate number of credit recovery students may be low-income. This may be due to my personal experience of working in a district where 80% of students are eligible for free and reduced priced lunch. Miron & Urschel (2012) found 39.9% of K12, Inc’s online students qualified for free or reduced-price lunch, compared with 47.2% for the same-state comparison group. In my district’s virtual academy, only 61% of students are classified as low-income, which is 19% lower than the district as a whole. Therefore, you might expect stronger academic performance from the virtual program, however, with a 630 API, the virtual program ranks 116 points below the District’s API of 746.
Decades of educational research have made it clear that low-income students are at the greatest risk for school failure. The ACT corroborates this in a recently released report: The Condition of College & Career Readiness 2013: Students From Low-Income Families. The authors used data from 1.8 million ACT-tested high school graduates from the US class of 2013. Of those, 428,549 (24%) were identified as being from low-income families. Nearly all (95%) of low-income students indicated they want to go to college, but only 69% took the recommended college prep curriculum in high school. Worse, only 20% of students met at least three of the four ACT College Readiness Benchmarks. Nearly half (49%) of students from low-income families did not meet any benchmarks.
It is difficult to understand why educational leaders keep pushing low-income students who are the least likely to be successful into virtual/online programs where ten years of data from California Community Colleges (the very places where most of the low-income students wind up) has demonstrated that 4 out of every 10 of them will fail. Perhaps it is time to end the credit recovery experiment in low-income schools. Reichert & Hawley (2014) argued that the teacher-student connection does not merely contribute to or enhance teaching and learning; this relationship is the very medium through which successful teaching and learning is carried out.
If policymakers were data-driven like teachers, they would understand that it is time to intervene and push virtual/online programs into affluent schools and ration our high-cost, high-touch, empathetic teaching talent for the struggle in low-income public schools. Instead, I suspect we will allow the free market to propagate the current policies that result in half of low-income students going 0 for 4 on ACT college benchmarks.
ACT (July 2014). The condition of college & career readiness 2013: Students from low-income families. Iowa City, IA. Accessed at http://www.act.org/newsroom/data/2013/states/pdf/LowIncomeStudents.pdf
City of Angels Virtual School & Independent Study Program. (2014). School profile:
City of Angels Virtual School & Independent Study Program. (2014).
Johnson, H. & Mejia, M. (2014). Online learning and student outcomes in California’s community colleges. The Public Policy Institute of California. San Francisco, CA. Accessed at http://www.ppic.org/main/publication.asp?i=1096
McCabe, J. and St. Andrie, R. (2012) Credit recovery programs. The Center for Public Education. Alexandria, VA. Posted January 26, 2012 at http://www.centerforpubliceducation.org/Main-Menu/Staffingstudents/Credit-recovery-programs/Credit-recovery-programs-full-report.html
Miron, G & Urschel, J. (2012). Understanding and improving full-time virtual schools. National Education Policy Center. Boulder, CO. Accessed at http://nepc.colorado.edu/files/nepcrbk12miron.pdf
Molnar, A. (2013). Virtual schools in the US: Politics, performance, and research evidence. National Education Policy Center. Boulder, CO. Accessed at http://greatlakescenter.org/docs/Policy_Briefs/Molnar_VirtualSchools.pdf