Using Team-Based Learning to Improve Learning and the Student Experience in a Mechanical Design Course
Bibliographic record
Abstract
In 2008, a design course on mechanical components (MECH 325) at the University of British Columbia was converted from a conventional lecture-based format to a team-based learning (TBL) format. The MECH 325 course is content-rich and covers the characteristics, uses, selection, and sizing of common mechanical components (including gears, flexible drives, bearings, and so on). With the shift in course format to TBL, student performance on exams as well as responses to teaching evaluations and course surveys all indicate an improvement in the students’ perception of the course and student learning. Specifically, performance on multiple choice exam questions from different years (remaining similar in both style and difficulty) increased by 17%. Likewise, on official University teaching evaluations over a five-year period, students rated the TBL version of the course as having a reduced workload, seeming less advanced, seeming more relevant, and being more interesting. On informal course surveys, 76% of students on average indicated they felt the various elements of TBL were effective towards the course aims. Finally, from instructor observations, the shift to TBL has resulted in increased student engagement and collaboration, and an increased emphasis on higher-level learning, such as application, synthesis, and judgment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".