Integrating syllabus data into student success models

Abstract

In this work, we present (1) a methodology for collecting, evaluating, and utilizing human-annotated data about course syllabi in predictive models of student success, and (2) an empirical analysis of the predictiveness of such features as they relate to others in modeling end-of-course grades in traditional higher education courses. We present a two-stage approach to (1) that addresses several challenges unique to the annotation task, and address (2) using variable importance metrics from a series of exploratory models. We demonstrate that the process of supplementing traditional course data with human-annotated data can potentially improve predictive models with information not contained in university records, and highlight specific features that demonstrate these potential information gains.