Collaborative exams: Cheating? Or learning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtually all human activity involves collaboration, and yet, collaboration during an examination is typically considered cheating. Collaborative assessments have not been widely adopted because of the perceived lack of individual accountability and the notion that collaboration during assessments simply causes propagation of correct answers. Hence, collaboration could help weaker students without providing much benefit to stronger students. In this paper, we examine student performance in open-ended, two-stage collaborative assessments comprised of an individually accountable round followed by an automatically scored, collaborative round. We show that collaboration entails more than just propagation of correct answers. We find greater rates of correct answers after collaboration for all students, including the strongest members of a team. We also find that half of teams that begin without a correct answer to propagate still obtain the correct answer in the collaborative round. Our findings, combined with the convenience of automatic feedback and grading of open-ended questions, provide a strong argument for adopting collaborative assessments as an integral part of education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it