Students’ online evaluation of teaching and system continuance usage intention: new directions from a multidisciplinary perspective
Bibliographic record
Abstract
Student evaluation of teaching (SET), a major tool for assessing teaching quality in higher education is a crucial research topic. Among 13 studies published about online peer SET in Assessment & Evaluation in Higher Education over the past two decades, ease of use, clarity and helpfulness of SET information were repeatedly tested. This study introduces theory from the information systems field to give a multi-disciplinary view in testing how students’ perceptions may affect their intention to continue to use an online peer SET system. While past studies focused on ratemyprofessor.com, offering results from USA, Canada and UK, this study aimed to provide Asian insight by using data from Taiwan. Based on 364 student members of the selected website, findings indicated that disconfirmation of SET information significantly affected perceived usefulness, trust and satisfaction, ultimately shaping continuance usage intention of the online peer SET system. Practical implications for online peer SET website managers and institutional SET managers are discussed.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".