‘What would my classmates say?’ An international study of the prediction‐based method of course evaluation
Bibliographic record
Abstract
OBJECTIVES: Traditional student feedback questionnaires are imperfect course evaluation tools, largely because they generate low response rates and are susceptible to response bias. Preliminary research suggests that prediction-based methods of course evaluation - in which students estimate their peers' opinions rather than provide their own personal opinions - require significantly fewer respondents to achieve comparable results and are less subject to biasing influences. This international study seeks further support for the validity of these findings by investigating: (i) the performance of the prediction-based method, and (ii) its potential for bias. METHODS: Participants (210 Year 1 undergraduate medical students at McGill University, Montreal, Quebec, Canada, and 371 Year 1 and 385 Year 3 undergraduate medical students at the University Medical Center Groningen [UMCG], University of Groningen, Groningen, the Netherlands) were randomly assigned to complete course evaluations using either the prediction-based or the traditional opinion-based method. The numbers of respondents required to achieve stable outcomes were determined using an iterative process. Differences between the methods regarding the number of respondents required were analysed using t-tests. Differences in evaluation outcomes between the methods and between groups of students stratified by four potentially biasing variables (gender, estimated general level of achievement, expected test result, satisfaction after examination completion) were analysed using multivariate analysis of variance (manova). RESULTS: Overall response rates in the three student cohorts ranged from 70% to 94%. The prediction-based method required significantly fewer respondents than the opinion-based method (averages of 26-28 and 67-79 respondents, respectively) across all samples (p < 0.001), whereas the outcomes achieved were fairly similar. Bias was found in four of 12 opinion-based condition comparisons (three sites, four variables), and in only one comparison in the prediction-based condition. CONCLUSIONS: Our study supports previous findings that prediction-based methods require significantly fewer respondents to achieve results comparable with those obtained through traditional course evaluation methods. Moreover, our findings support the hypothesis that prediction-based responses are less subject to bias than traditional opinion-based responses. These findings lend credence to prediction-based as an accurate and efficient method of course evaluation.
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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 | 0.088 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".