Two-Stage CS Exams: A Multi-Institutional Study of Course and Exam Feature Impacts on Student Perceptions and Performance
Bibliographic record
Abstract
A two-stage exam (TSE) is an exam format where students first complete an exam individually before completing the same (or a similar) exam in collaboration with a group of peers. Our working group collected data from Computer Science instructors deploying TSEs at a variety of post-secondary institutions in Canada, the United States, and Australia. We conducted quantitative analysis of student perception and performance data and synthesized results with qualitative analysis of student and instructor perceptions. Our mixed methods approach enabled us to identify several important factors influencing student perceptions of and performance on TSEs in Computer Science classes, including group formation method and group size during the TSE. Student survey responses indicated that students generally had very positive perceptions about TSEs, and a majority of students felt better about their performance on the individual stage after completing the group stage. Some instructors experimented with offering a different exam during the group stage: students who completed the same exam during both stages of the TSE reported more positive perceptions about the experience than students in courses where the group stage exam differed from the individual stage exam. TSE grade data compared with students' grades going into the assessment show that while the raw group-vs-individual grade differentials are highest for low-performing students, when we consider the relative grade improvement, it is similar across the performance spectrum. Instructors were positive about the experience of running TSEs and all reported intentions to continue using the assessments in at least some courses.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".