Panel Paper: Identifying Student-Level Factors Associated with Success in Accelerated Models of Developmental Education: A Regression Discontinuity Approach

Friday, November 3, 2017
Haymarket (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Russell Gerber1, Trey Miller2, Emily Weisburst3, Paco Martorell4 and Lindsay Daugherty2, (1)Texas Higher Education Coordinating Board, (2)RAND Corporation, (3)University of Texas, Austin, (4)University of California, Davis


Faced with growing evidence that traditional course-based models for developmental education have been ineffective; researchers, policymakers and practitioners nationwide have engaged in research-based reform efforts to transform the way that colleges support underprepared college students. Acceleration models are one prominent class of interventions that have emerged. Acceleration models place non-college-ready students directly into a college level course in their first semester, and often pair that course with a support course or intervention to help address their academic deficiencies. A small but growing body of research suggests that acceleration models show promise for improving a range of student outcomes. However, little is known about the types of students who are likely to benefit from this type of intervention. This study draws upon rich administrative data from the state of Texas to help identify student characteristics related to success when students are accelerated. Specifically, we look at the impact of being placed in college-level courses in Math and English, as opposed to developmental education options for students with a range of different characteristics that we can observe in statewide administrative data. We use a regression-discontinuity design that exploits a strict cutoff on the statewide assessment that institutions were required to use for past cohorts to address selection into college-level courses, and look at treatment effects interacted with observable student level characteristics. Preliminary results suggest that older students may be less likely than traditional-aged students to benefit from acceleration in Math, but more likely to benefit from acceleration in Reading and Writing. Students enrolled less than full time are less likely than full time students to benefit from acceleration in all areas. Finally, we find that English language learners are more likely than fluent English speakers to benefit from acceleration in Math, but less likely to benefit from acceleration in Reading and Writing. We are currently working on linking additional data sources to examine how additional student-level factors relate to success when placed in college level courses.