A Study of the Correlation of the Improvement of Teaching Evaluation Scores Based on Student Performance Grades
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it