More than ever before, educators and researchers are keeping a keen eye on student college- and career-readiness. The widely adopted Common Core State Standards were written with the explicit goal of helping students to be college- or career-ready by the time they graduate from high school. However, many students experience setbacks, such as course failure, within their educational career placing them at risk for not reaching this goal. Because the ACT can predict student success in college, states often use benchmark scores from the exam to measure student college- and career-readiness. A student who fails to learn fundamental concepts in either Algebra I or Geometry will not score as well on the ACT and is not likely to meet benchmark scores for college- and career-readiness. It is important, then, for schools to provide credit recovery opportunities to students who do not pass these classes so they can master the content and earn a passing grade.
This research study examines different credit recovery options offered at one high school to students who failed Algebra I and/or Geometry. These options included re-taking the class, summer school, an online course, and a more unique mastery based program. Because students were nested within teachers, hierarchical linear modeling was used to determine associations between credit recovery options and the ACT mathematics score which is used to determine college- and career-readiness. Also considered were the effects of gender, race, socioeconomic status, and previous achievement indicated by PLAN mathematics scores. For Algebra I, no variables were found to be statistically significant as fixed effects, and only re-taking the class, PLAN mathematics scores, and identification as White were found to be statistically significant as random effects. For Geometry, identification as being African American was the only variable found to be statistically significant as a fixed effect, and re-taking the course and participation in summer school were both found to be statistically significant as random effects.