Bibliographic record
Abstract
The call for teaching accountability in higher education initiated teaching effectiveness research and its scales development. Attention in many institutions of higher learning has been diverted recently to the improvement of teaching performance as another way besides academic research to promote the higher institutions. The diversity of attention is a response to external calls for accountability in teaching as a result of the under-estimation of the significance of the teaching process compared to research activities. As research on teaching effectiveness has increased, so has the number of different measures of teaching effectiveness. Hence, in this article, the researchers examined the psychometric properties of two teaching effectiveness scales, namely the Marsh Student Evaluation of Educational Quality (1987) and Mahfooz Ansari and Mustafa Achoui Ansari Teaching Feedback Survey (2000) in terms of their factorial and construct validity. A total of 1504 3rd and 4th year and postgraduate students were selected from four renowned Malaysian public Universities, namely USIM, UM, UPM and IIUM. The study found that although the two scales were constructed to assess teaching effectiveness in higher institutions, the Marsh scale was extensively used in the literature and more comprehensive in relation to the numbers of factors. The study found that although there is room for improvement for both scales, the Marsh’s scale is psychometrically more sound, and theoretically more comprehensive than Ansari and Ansari’s scale.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".