Student Evaluation of Lecturers – What do Faculty Members Think about the Damage Caused by Teaching Surveys?
Bibliographic record
Abstract
Many studies have been conducted on teaching evaluations and student surveys. The current study is unique for examining, by means of direct questions, the meaning of teaching surveys as perceived by academic faculty in Israel. Senior faculty members at academic institutions completed questionnaires, with a total of 182 questionnaires collected. We employed mixed research methods, beginning with qualitative analysis followed by Structural Equation Modeling (SEM), with the goal of developing a model that reflects faculty members’ beliefs on teaching surveys. The research findings show that the lecturers find that student evaluations are detrimental to their relationship with their students, and adversely affect their teaching practice and interpersonal interactions with their students. In view of the importance attributed to students' voices and their opinions of teaching, the question is how should these evaluations be addressed, Do teaching surveys constitute a reliable managerial tool and a foundation for improving teaching – or should other tools be developed to improve teaching practices, independent of students' opinions?
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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 | 0.042 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".