Predicting undergraduate student evaluations of teaching using probabilistic machine learning: The importance of motivational climate

Abstract

The purpose of this study was to understand the complex interactions within a course among motivational climate variables and student evaluations of teaching (SETs) by developing online simulators using probabilistic machine learning. We used data from 2938 undergraduate students in 30 classes to create online simulators based on Bayesian Belief Networks. We created bubble charts, line graphs, and radar charts to show the relationships between the study variables. Findings showed that (a) the motivational climate variables—as measured by the MUSIC Model of Motivation variables—are the largest predictors of SETs, (b) student interest (in the coursework and instructional methods) is the overall largest predictor of SETs, (c) the relationships between the motivational climate variables and SETS are nonlinear, (d) the ease of the course and demographic variables are only weakly associated with SETs, and (e) the largest predictors of instructor and course rating are similar, but somewhat different.