Using Team-Based Learning to Improve Learning and the Student Experience in a Mechanical Design Course
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
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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