A Study of the Correlation of the Improvement of Teaching Evaluation Scores Based on Student Performance Grades
Bibliographic record
Abstract
The purpose of the study is to explore the influence of teaching evaluations on teachers in that they might try to please their students by giving higher grades in order to get higher teaching evaluation scores. To achieve this purpose, the study analyzed the correlations between teaching evaluation scores, student’s final grades and course fail rates, and it also examined whether students’ final scores and course fail rates are important predictors of teaching evaluation scores. The study used teaching evaluation scores and students’ final grades of the courses offered in the fall term of academic year 2014 and the spring term of academic year 2015 in one university in Taiwan as research samples. The results showed that both student’s final grades and course fail rates are predictors of teaching evaluation scores. There is a positive correlation between teaching evaluation scores and students’ final grades, and a negative correlation between teaching evaluation scores and course fail rates. Based on the findings, the study inferred that the implementation of teaching evaluations may influence teachers to give better grades and lower course requirements to please their students in order to get higher teaching evaluation scores.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".