Response Rate and Teaching Effectiveness in Institutional Student Evaluation of Teaching: A Multiple Linear Regression Study
Bibliographic record
Abstract
<p>It is important to consider the question of whether teacher-, course-, and student-related factors affect student ratings of instructors in Student Evaluation of Teaching (SET) in English Language Teaching (ELT). This paper reports on a statistical analysis of SET in two large EFL programmes at a university setting in the Sultanate of Oman. I carried out a multiple regression analysis to address the research questions of whether instructor sex, class size, course type and percent participation would affect teaching effectiveness scores, and whether or not response rate can be predicted by instructor sex, class size and course type. The study utilizes a dataset of over 2000 student ratings obtained from an SET survey covering the period from Fall 2011 through to Spring 2014in these two programmes. Results indicated that the modeled predictors showed extremely low bias towards both teaching quality scores and response rate. Although the effect sizes of these results are extremely small, they are still significant due to the large sample size (comprising over 2000). The findings also suggest that contrary to common parlance in some quarters claiming students’ unreliable ratings, this analysis has shown that students can judge teaching effectiveness and do not allow other teacher-, course- and student-related factors to bias their responses. The study’s significance stems from the fact that it adds to instructional evaluation in ELT, a field characterized by a clear lack of research on SET.</p>
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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 | 0.055 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".