SUCCESSES WITH TWO-STAGE EXAMS IN MECHANICAL ENGINEERING
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
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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