Many high schools use online courses to allow students to retake failed classes in an effort to help get students back on track and graduate. However, there is limited evidence available on the effectiveness of online credit recovery in improving students’ long-term outcomes compared with traditional face-to-face credit recovery courses. In this paper, we examine longer term outcomes for ninth graders who failed Algebra I and were randomly assigned to an online or face-to-face algebra credit recovery course. In particular, we look at math credits earned through four years of high school and rates of on-time graduation. We find no statistically significant differences in longer term outcomes between students in the online and face-to-face courses. Implications of these null findings are discussed.
AllensworthE. M.EastonJ. (2005). The on-track indicator as a predictor of high school graduation. Chicago, IL: Consortium on Chicago School Research.
2.
AllensworthE.EastonJ. Q. (2007). What matters for staying on-track and graduating in Chicago Public High Schools: A close look at course grades, failures, and attendance in the freshman year. Chicago, IL: Consortium on Chicago School Research.
ArchambaultL.DiamondD.CoffeyM.Foures-AalbuD.RichardsonJ.Zygouris-CoeCavanaughC. (2010). INACOL research committee issues brief: An exploration of at-risk learners and online education. Vienna, VA: International Association for K–12 Online Learning.
BakiaM.MislevyJ.HeyingE.PattonC.SingletonC.KrummA. (2013). Supporting K–12 students in online learning: A review of online Algebra I courses. Menlo Park, CA: SRI International.
7.
BakiaM.ShearL.ToyamaY.LasseterA. (2012). Understanding the implications of online learning for educational productivity. Washington, DC: U.S. Department of Education, Office of Educational Technology. Retrieved from https://tech.ed.gov/files/2013/10/implications-online-learning.pdf
8.
BaileyD.DuncanG. J.OdgersC. L.YuW. (2017) Persistence and fadeout in the impacts of child and adolescent interventions. Journal of Research on Educational Effectiveness, 10(1), 7–39.
ClementsM.PazzagliaA. M.ZweigJ. (2015). Online course use in New York high schools: Results from a survey in the Greater Capital Region (REL 2015–075). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast & Islands.
11.
ClementsM.StaffordE.PazzagliaA. M.JacobsP. (2015). Online course use in Iowa and Wisconsin public high schools: The results of two statewide surveys (REL 2015–065). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Midwest.
12.
DynarskiM.ClarkeL.CobbB.FinnJ.RumbergerR.SminkJ. (2008). Dropout prevention: A practice guide (NCEE 2008–4025). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
13.
FrazelleS. (2016). Successful strategies for providing online credit recovery in Montana. Portland, OR: Education Northwest, Regional Educational Laboratory Northwest.
14.
GeminB.PapeL.VashawL.WatsonJ. (2015). Keeping pace with K–12 digital learning: An annual review of policy and practice. Evergreen, CO: Evergreen Education Group.
15.
GreavesT. W.HayesJ. (2008). America’s digital schools 2008: Six trends to watch. Encinitas, CA: The Greaves Group.
16.
HeppenJ. B.SorensenN.AllensworthE.WaltersK.RicklesJ.TaylorS. S.MichelmanV. (2017). The struggle to pass algebra: Online vs. face-to-face credit recovery for at-risk urban students. Journal of Research on Educational Effectiveness, 10(2), 272–296. doi:10.1080/19345747.2016.1168500
17.
HughesJ.ZhouC.PetscherY. (2015). Comparing success rates for general and credit recovery courses online and face to face: Results for Florida high school courses (REL 2015–095). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southeast.
18.
JonesM. P. (1996). Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Statistical Association, 91(433), 222–230.
19.
KempleJ.HerlihyC.SmithT. (2005). Making progress toward graduation: Evidence from the talent development high school model. New York, NY: Manpower Demonstration Research Corporation.
20.
LevinH. M.BelfieldC. R.MuennigP.RouseC. E. (2007). The costs and benefits of an excellent education for all of America’s children. New York, NY: Center for Benefit-Cost Studies in Education, Teachers College, Columbia University.
21.
LevineE.JohnsonJ.MalaveJ.SantanielloS. (2017). Online courses for credit recovery in high schools: Effectiveness and promising practices. Quincy, MA: Nellie Mae Education Foundation.
MayerR. E. (2011). Applying the science of learning. Upper Saddle River, NJ: Pearson.
24.
MayerR. E.MorenoR. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.
25.
McCaffreyD. F.RidgewayG.MorralA. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403–425.
26.
MeansB.ToyamaY.MurphyR.BakiaM. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115, 1–47.
QueenB.LewisL. (2011). Distance education courses for public elementary and secondary school students: 2009–10 (NCES 2012–008). Washington, DC: U.S. Department of Education, National Center for Education Statistics.
30.
RicklesJ.HeppenJ.TaylorS.AllensworthE.MichelmanV.SorensenN.ClementsP. (2016). Getting back on track: Who needs to recover algebra credit after ninth grade?Washington, DC: American Institutes for Research.
31.
RidgewayG.McCaffreyD.MorralA.GriffinB. A.BurgetteL. (2013). Twang: Toolkit for weighting and analysis of nonequivalent groups. R package version 1.3–20. Retrieved from https://cran.r-project.org/web/packages/twang/index.html
32.
StakerH.HornM. B. (2012). Classifying K–12 blended learning. Lexington, MA: Innosight Institute.
33.
StallingsD. T.WeissS. P.MaserR. H.StanhopeD.StarckeM.LiD. (2016). Academic outcomes for North Carolina Virtual Public School credit recovery students (REL 2017–177). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southeast.
34.
StevensD.FrazelleS. (2016). Online credit recovery: Enrollment and passing patterns in Montana Digital Academy courses (REL 2016–139). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northwest.
35.
StewartB. L.GoodsonC. E.MiertschinS. L.NorwoodM. L.EzellS. (2013). Online student support services: A case based on quality frameworks. Journal of Online Learning and Teaching, 9(2), 290–303.
36.
TaylorS.ClementsP.HeppenJ.RicklesJ.SorensenN.WaltersK. . . . MichelmanV. (2016). The role of in-person instructional support for students taking online credit recovery. Washington, DC: American Institutes for Research.
37.
U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: Author.
38.
WaltersK.HeppenJ.TaylorS.SorensenN.RicklesJ.ClementsP. (2016). Getting back on track: What math content is taught and learned in online and face-to-face algebra credit recovery courses?Washington, DC: American Institutes for Research.
What Works Clearinghouse. (2014). Assessing attrition bias. Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved from http://ies.ed.gov/ncee/wwc/documentsum.aspx?sid=243
