The Influence of Two-Stage Collaborative Testing on Peer Relationships: A Study of First-Year University Student Perceptions
Bibliographic record
Abstract
Two-stage testing is a form of collaborative assessment that creates an active learning environment during test taking. In two-stage testing, students first complete an exam individually, and then complete a subset of the same questions as part of a learning team with the ultimate exam score being a weighted average of the individual and team portions. In the second (team-based) part of the exam, students are encouraged to discuss solutions until a consensus among team members is achieved, thus actively engaging students with course material and each other during the exam. A short open-ended survey was administered to students at the end of the semester, and the responses coded by thematic analysis, with themes generated using inductive coding based on the principles of grounded theory. The most important conclusion was that students overwhelmingly preferred two-stage tests for the development of positive peer relationships in class. The most common themes that emerged from student responses involved positive feelings from forced interaction with their peers, the benefits of meeting and socializing with other students, sharing of knowledge with others, and solidarity or positive affect towards the process of working as part of a team. Finally, students also expressed an overall preference for two-stage exams when compared to solely individual, one-stage exams.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".