Analysis of Student Grades after Switching to POGIL

Abstract

Process Oriented Guided Inquiry Learning (POGIL) is becoming increasingly popular in computer science as an evidence-based teaching strategy. From 2017-2022, the IntroCS-POGIL project supported faculty at higher-ed institutions in the United States to adopt POGIL in their introductory CS courses. The faculty participated in summer workshops and mentoring groups during the academic year. At the end of each semester, the faculty submitted a summary of their students' grades to the research team. This poster presents a Bayesian analysis of the student grades using a hierarchical ordinal logistic regression model. The data included the number of A, B, C, D, F, and W grades, disaggregated by gender and ethnicity, for all students enrolled in the course. In addition to each POGIL semester, faculty submitted grades for one or two previous semesters when they taught the same course without POGIL. The poster will summarize the regression model and present a detailed visualization of 12 faculty that highlight positive and negative results.