The purpose of this study is to evaluate the effectiveness of developmental math placement policies on student success in community college. The main research question that guides the study is: What are the effects of placement decisions on the educational outcomes of community college students? To answer this question we draw on economic and governance theories and employ quantitative and qualitative methods to explore how assessment and placement policies are designed and implemented and how effective they are in ensuring student progress through the developmental math sequence.

The contents of this website were developed under grant #R305A100381 from the U.S. Department of Education. However, those contents do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal Government.

  1. Description of the assessment and placement (A&P) policies: The decentralized nature of the governance structure of the LACCD implies that each college in the district has the discretion to set the A&P policies that best fit the students in their colleges. This has resulted in very different A&P policies in each of the nine colleges that we document in great detail.
  2. Evaluate A&P policies for developmental math on student success in developmental education trajectory: We used a regression-discontinuity design (RDD) within a time hazard model to estimate the effect of assignment to different levels of the preparatory math trajectory on short- and long-term educational outcomes. The outcomes we examined relate to a student’s academic performance and include:
  • passing the subsequent course of the sequence
  • accumulating over 30 credits in degree applicable courses
  • accumulating over 30 credits in transfer-level courses

We chose a RDD approach because this is a technique that enables the researcher to make causal inferences when randomization is not feasible. The general idea is that the researcher “assigns” individuals to the treatment and control groups rather than the fair coin toss that is common in randomized trials. Employing a regression discontinuity design, we run an intent to treat (ITT) analysis to measure the effectiveness of assessment and placement policies on student success. We also contribute to the literature by running a hazard model to estimate the effectiveness of these policies over time as well as evaluate how the impact of these policies varies across the courses that constitute the math sequence.