Jones, Brett D.; Topuz, Kazim; Sahbaz, Sumeyra (2024). Predicting undergraduate student evaluations of teaching using probabilistic machine learning: The importance of motivational climate. Studies in Educational Evaluation, 81. 10.1016/j.stueduc.2024.101353
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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.
Item Type: |
Journal Article (Original Article) |
|---|---|
PHBern Contributor: |
Sahbaz, Sumeyra |
ISSN: |
0191-491X |
Language: |
English |
Submitter: |
Léanne Zbinden |
Date Deposited: |
19 Aug 2024 09:47 |
Last Modified: |
26 Sep 2024 13:21 |
Publisher DOI: |
10.1016/j.stueduc.2024.101353 |
Uncontrolled Keywords: |
Teacher evaluation, Student evaluation, Evaluation methods, Student motivation, Student engagement |
PHBern DOI: |
10.57694/7511 |
URI: |
https://phrepo.phbern.ch/id/eprint/7511 |
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