An Analysis of Grades, Class Level and Faculty Evaluation Scores in the United Arab Emirates
Bibliographic record
Abstract
This study examined the results of a student evaluation of faculty against the grades awarded and the level of the course for a higher education institution in the United Arab Emirates. The purpose of the study was to determine if the grades awarded in the course and/or level of the course impacted the evaluation scores awarded to the faculty member. The study utilized a 25-question student perception survey coupling course results with the overall course grade point average (GPA) and the course level. All courses were undergraduate. Descriptives for the responses were obtained prior to conducting a factor analysis for the purposes of dimension reduction. The analysis included 184 course pairings. The data set was examined to verify satisfaction of assumptions appropriate for factor analysis. Reliability analysis yielded a Chronbach’s alpha of 0.974. The factor analysis identified three underlying factors accounting for 80.07% of the variance. These three factors were identified as (1) overall perception of instruction, (2) the relationship of the grade and course level and (3) course management. Results of the study did not indicate that the grades given in a class nor the level of the course significantly affected the evaluations provided by the students. Grades and the level of the course were found to align. Student achievement in the course was also found to relate to the student’s perception of fair treatment by the faculty member.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".