Transition from Paper to Online Course Evaluation: Preliminary Trends in Student Response Rate and Overall Professor Evaluation
Bibliographic record
Abstract
Student evaluation of teaching (SET) hasbeen used as a metric to arguably evaluate instructoreffectiveness and quality of instruction since the 1920s.SET is used in decisions regarding annual evaluation (offaculty) and is one of the most researched topics inevaluation of instructor effectiveness. Central researchquestions associated with SET include whether SET is anappropriate measure of effectiveness and whether it leadsto improved teaching and quality of graduates.In the fall 2013, the Faculty of Engineering at theUniversity of Alberta for the first time administered SETonline. The transition from paper-based and in class SETto online and out of class SET provides a uniqueopportunity to investigate changes in SET response rateand ratings of overall instructor effectiveness that couldbe attributed to change in protocol and that wouldsuggest protocol-related bias. Our preliminary resultsshow lower response rates for online SET andeffectiveness scores that were outside one standarddeviation of the previous 5-year mean. These findingsshow the importance of continuing to monitor web-basedSET results and point to directions of further research. Atthis time, this will not be possible, as the Faculty ofEngineering has discontinued online SET testing after asingle term.
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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 | 0.044 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".