Strengthening Students Mechanics Knowledge through Instructional Videos of Hands-on Activities
Bibliographic record
Abstract
The use of hands-on activities has been proven in the past to be effective in teaching pedagogies. Recognizing this need, a first year Mechanics course at the University of Waterloo has already implemented the use of seven hands-on activities. Instructors of the course have found certain time limitations which results in students only participating in two of the seven activities. To continue improving student learning, instructional videos were developed to solve this problem. The techniques used for video development incorporate learning pedagogies to foster deeper learning throughout the viewing experience. These techniques include simulating experiential learning and reflective learning. In each video, a breakdown of the activity building and experimenting process is demonstrated. This is done through people physically interacting with the models as students would in the classroom. Accompanying the demonstration is an illustration of various mistakes students often make during the activities. Errors are discussed, and their outcomes are shown using course concepts to reinforce the appropriate processes. In addition, questions are posed to the viewers throughout each of the videos.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".