Use of student ratings to benchmark universities: Multilevel modeling of responses to the Australian Course Experience Questionnaire (CEQ).
Bibliographic record
Abstract
Recently graduated university students from all Australian Universities rate their overall departmental and university experiences (DUEs), and their responses (N = 44,932, 41 institutions) are used by the government to benchmark departments and universities. We evaluate this DUE strategy of rating overall departments and universities rather than individual teachers, and we juxtapose it with the traditional use of student ratings to evaluate individual teachers (SETs). Multilevel analyses of DUE overall ratings were not able to discriminate well between universities or departments--few universities or departments differed significantly from the grand mean. Although the a priori 5-factor structure for this DUE instrument was reasonably well-defined at the individual student level, none of the 5 factors separately or in combination discriminated well between departments or universities. In contrast to this pattern of results, we review studies showing that SETs do reliably differentiate between teachers and are valid in relation to many criteria of effective teaching. However, casual reviews of these research literatures should not use this support for SETs to justify the use of DUE-type strategies. We conclude that DUE-type ratings should be used with great caution, if at all, and should not be seen as an alternative to SETs
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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 | 0.048 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".