Student Evaluation of Lecturer Performance Among Private University Students
Bibliographic record
Abstract
The evaluation of lecturer performance at the end of the semester is widely practicedby learning institutions and universities. The results of the evaluations are beneficial in understanding the areas of possible improvement for the lecturer. The purpose of this study is to identify the factors and predictors of lecturer performance among undergraduates in a private university in Malaysia using the existing questionnaire. A total of 223 respondents were recruited using multistage sampling.The results of this study showed that lecturer and tutor characteristics ( r = 0.722, p < 0.01), subject characteristics ( r = 0.699, p < 0.01), the studentship ( r = 0.472, p < 0.01), and learning resources and facilities ( r = 0.650, p < 0.01) were positively correlated with overall lecturer performance. Stepwise hierarchical regression was used to determine the predictors of overall lecturer performance among the students. The results of the final model showed that lecturer and tutor characteristics, subject characteristics, and learning resources and facilities explained 61.9% of the variance in overall lecturer performance among students ( F = 118.732, p < 0.01). Knowing the predictors of overall lecturer performance would help the lecturer and university identify the specific areas for improving the performance of the lecturer. Key words : Lecturer and tutor characteristics; Subject characteristics; Learning resources and facilities; Overall performance
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".