Use of student ratings to benchmark universities: Multilevel modeling of responses to the Australian Course Experience Questionnaire (CEQ).
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it