SUCCESSES WITH TWO-STAGE EXAMS IN MECHANICAL ENGINEERING
Bibliographic record
Abstract
Two-stage exams consist of a traditionalpencil-and-paper examination written in class byindividual students, followed immediately by a secondsitting in which the students retake the same exam inteams (i.e. a collaborative test). The team test providesan immediate opportunity for students to discuss, debate,teach, and receive feedback on the subject matter. Itdraws on principles of goal-directed practice, timelytargeted feedback, and collaborative learning.The practice of two-stage testing is a defining featureof the Team-Based Learning approach, and is used forintroductory reading quizzes that begin each coursemodule. These have been part of the instructionalapproach in Mechanical Engineering at the University ofBritish Columbia for over a decade. In 2014, we haveextended two-stage testing to include midterm and finalexaminations. To accommodate the team portion, examswere shortened by approximately one third and questionswere reformatted to be easier to complete in teams.Students report a strong preference this approach(72% in favour) and report a resulting improvement intheir understanding of the course material (75%). Examperformance gains have also been observed. In almost allcases, teams outperform their strongest member, and it isnot uncommon that the weakest team outperforms thestrongest individual in the class. As an added benefit, therevised question structure that makes it easier for studentsto collaborate on exam writing has also simplified andexpedited the marking process.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".