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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".