I flipped my tutorials: a case study of implementing active learning strategies in engineering
Bibliographic record
Abstract
A variety of active learning strategies havebeen applied to engineering classrooms, includingflipping classrooms by recording lectures and havingstudents watch them outside of class time. In this study, asimilar approach was used for long-answer problemspresented in one-hour tutorial sessions. Problemsolutions were recorded and made available online.Instead of solving long-answer problems, tutorials beganwith a review of relevant material. The review was thenfollowed by independent working time where studentswere free to interact with the teaching assistant anddiscuss concepts with one another while working on anonline quiz.Students generally responded very positively tothe changes and appreciated the ability to go throughproblem solutions at their own pace with the recordings.In tutorials, the quizzes were successful at encouragingdiscussion of course content amongst students. Thetechniques also provided a repository of online videosand quizzes to be used in future course iterations.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| 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".