Improving the effectiveness of teacher assessment in higher education: a case study of professors’ perceptions in Morocco
Bibliographic record
Abstract
Abstract Teacher assessment in higher education is a widely used strategy in Western countries, but it is not common practice in Moroccan academia. In consequence, the establishment of a new assessment policy in higher education creates a new dynamic in teaching practices, teacher assessment, and professional career development. This type of major change, however, is likely to generate anxiety among staff regarding the learning of new practices, and uncertainty regarding their involvement in the process of teacher assessment. The objective of the present study was thus to explore the perceptions of professors concerning the implementation of teacher assessment in Moroccan universities through a survey and interviews carried out among the professors. The majority of professors expressed a positive attitude toward the usefulness of teaching evaluations. According to them, teacher assessment helps them identify strengths and weaknesses in their teaching. In contrast, there is a recurring issue among the respondents, namely the crisis of trust toward the parties involved in the evaluation process and apprehension regarding consequences of these evaluations’ results on faculty status and rank progression. The majority were very suspicious of using student assessment for administrative purposes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".