How do Academic Faculty Members Perceive the Effect of Teaching Surveys Completed by Students on Appointment and Promotion Processes at Academic Institutions? A Case Study
Bibliographic record
Abstract
It is commonly thought that the promotion of faculty members is affected by their research performance. The current study is unique in examining how academic faculty members perceive the harm or damage to academic appointment and promotion processes, as a direct effect of student evaluations as manifested in teaching surveys. One hundred eighty two questionnaires were collected from senior faculty members at academic institutions. Most respondents were from three institutions: Ariel University, Ben Gurion University, and the Jezreel Valley College. Qualitative and statistical research tools were utilized, with the goal of forming a model reflecting the effect of the harm to academic appointment and promotion processes, as perceived by faculty members. The research findings show that the lecturers find an association that causes harm to their promotion processes as a result of student evaluations. Assuming that students' voices and their opinion of teaching are important – the question is how should these evaluations be treated within promotion and appointment processes: what and whom do they indicate? Do they constitute a reliable managerial tool with which it is possible to work as a foundation for promotion and appointment processes – or should other tools be developed, unrelated to 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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".