Online Credit Recovery in a Large School Division in Virginia: Examining Factors for Participation and On-Time Graduation

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Virginia Tech


Under the pressure of federal accountability for high schools in the United States to improve and maintain high rates of on-time graduation, online credit recovery has become an increasingly popular intervention to help students earn credit in a course that they have previously failed. While some studies have connected online credit recovery with positive outcomes for participants, others have found negative outcomes and poor learning experiences. Set in a large school division in Virginia, the purpose of this study was to (a) identify explanatory student factors that were associated with participation in online credit recovery and (b) compare the likelihood of on-time graduation of participants with the likelihood of on-time graduation of nonparticipants. Limited to the graduation cohorts of 2019 and 2020, there were 10,010 students in the sample from the participating school division. In the sample, 27% of students were eligible to participate in online credit recovery, but only 2.3% of students participated. Binary logistic regression models were designed to identify factors associated with participation and the likelihood of on-time graduation. Covariates considered for inclusion in the model were gender, race and ethnicity, status as an English learner, status as a student with a disability, status as homeless, status as economically disadvantaged, high school grade point average, and school. Both models failed to meet goodness of fit standards and were rejected as having fit the data. No student factors were found to have explained participation, and differences in the likelihood of on-time graduation were not identified. These findings indicated that there did not appear to be systemic participation given the studied factors, reinforced by the finding that participation was relatively uniformly distributed among the schools. The finding of a lack of significant difference in the likelihood of on-time graduation highlighted flexibility for schools in choosing their recovery interventions. State agencies may also consider collecting and publicly reporting data about student participation in online credit recovery. Opportunities for future studies include replication in other settings, particularly districts of different size and area/region, and qualitative inquiry into decisions made by school and district leaders related to credit recovery.



at-risk students, course failure, credit recovery, high school, logistic regression, graduation, online learning