Addressing Common Concerns about Online Student Ratings of Instruction: A Research-Informed Approach
Bibliographic record
Abstract
Concerns over the usefulness and validity of student ratings of instruction (SRI) have continued to grow with online processes. This paper presents seven common and persistent concerns identified and tested during the development and implementation of a revised SRI policy at a Canadian research-intensive university. These concerns include bias due to insufficient sample size, student academic performance, polarized student responses, disciplinary differences, class size, punishment of rigorous instructor standards, and timing of final exams. We analyzed SRI responses from two mandatory Likert scale questions related to the course and instructor, both of which were consistent over time and across all academic units at our institution. The results show that overall participation in online SRIs is representative of the student body, with academically stronger students responding at a higher rate, and the SRIs, themselves, providing evidence that may moderate worries about the concerns.
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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 | 0.385 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".