Evaluating Simultaneous Group Activities Through Self- and Peer-Assessment: Addressing the "Evaluation Challenge" in Active Learning
Bibliographic record
Abstract
Instructors seeking to add active learning elements to their courses encounter an “evaluation challenge” when trying to assign grades to discussion-based activities that do not produce a final product. By creating a way to incorporate evaluation into hard-to-observe activities, the protocol presented here can help instructors make active learning elements a key part of the evaluation of courses and, by providing a simple framework, reduce time spent marking. Drawing on debates in the scholarship of teaching and learning focused on reducing bias and grading irregularities in peer-evaluation, and building directly on Lawrence Li’s normalization protocol, this procedure combines marks from both self- and peer-evaluations, controlling for irregular grading practices and differences in subjective marking “toughness.” As the community of the scholarship of teaching and learning in politics and international relations continues to grow, continued attention on evaluation can help ensure that this important element of pedagogical practice can be improved to better fit the realities of today’s classroom (real or virtual).
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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 | 0.127 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".