Student personality differences are related to their responses on instructor evaluation forms
Bibliographic record
Abstract
The relation of student personality to student evaluations of teaching (SETs) was determined in a sample of 144 undergraduates. Student Big Five personality variables and core self-evaluation (CSE) were assessed. Students rated their most preferred instructor (MPI) and least preferred instructor (LPI) on 11 common evaluation items. Pearson and partial correlations simultaneously controlling for six demographic variables, Extraversion, Conscientiousness and Openness showed that SETs were positively related to Agreeableness and CSE and negatively related to Neuroticism, supporting the three hypotheses of study. Each of these significant relations was maintained when MPI, LPI or a composite of MPI and LPI served as the SET criterion. For example, the MPI-LPI composite correlated .28 with Agreeableness, .35 with CSE and –.28 with Neuroticism. Similar correlations resulted for MPI and LPI. Hierarchical multiple regression demonstrated that the CSE was an independent predictor of MPI ratings, Agreeableness was an independent predictor of LPI ratings, and both the CSE and Agreeableness were independent predictors of MPI-LPI composite ratings. Neuroticism did not emerge as an independent predictor because of the substantial correlation between CSE and Neuroticism (r = .53) and because CSE had greater predictive capacity. This is the first study to incorporate the CSE construct into the SET literature.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".