Examining Online Course Evaluations and the Quality of Student Feedback
Bibliographic record
Abstract
The purpose of this article was to provide a comprehensive review of research on the quality of student feedback from post-secondary institutions using online course evaluations versus traditional paper-pencil methods. Nineteen peer-reviewed articles published from 2000 to 2020 were examined for changes to course evaluations following a transition to online collection methods. Three themes emerged from the literature: effects on response rates, presence of non-response bias, and effects on comment quality. Results suggest that using online methods for collecting student feedback tends to decrease response rates somewhat, however, the effect is often temporary. Further, using online methods generated conflicting results on the presence of a non-response bias in open-ended comments with online methods. Many studies demonstrated that online methods increase the word counts in student-provided comments and that the constructive nature of the comments improved as well. The results may inform teaching and policy decisions as more institutions transition to online course evaluation collection methods, particularly given the restrictions imposed by the current COVID-19 crisis. Suggestions for future research include examining the usability of comments as well as trends in student feedback quality following the transition to emergency remote teaching during the global pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".