Bibliographic record
Abstract
The first course in Dynamics can be a challenging one for many undergraduate engineering students. Concepts can be complex, mathematical treatments can be non-trivial, and the theory can be difficult to apply. While lectures introduce course material, tutorials are often used for textbook problem solving and labs allow for an experiential exploration of the concepts. However, given the volume of material covered in Dynamics, the relative infrequency of labs, and their limited duration, one could argue that labs do not adequately fulfill their important role. With this perspective in mind, a redesign of this course took place so that in each lecture, one or more concepts were visually demonstrated and/or students took part in an activity to illustrate the concepts. This was in addition to the regular lab experiences. Furthermore, many of the demonstrations were taken from the world of sports to provide both accessible relevance and reinforcing examples. Feedback from students suggests that this approach is both helpful and motivating. Students report “getting it” through these demonstrations and enjoying the learning experience more than conventional approaches. Students were asked which “daily dynamic demos” they preferred, and the reasons for those preferences as a matter of course development. It appears that the richer, more personally relevant and more interactive the demonstrations, the more impact they had. This paper describes the various demonstrations and lab exercises, the motivations behind using each of them, and the concepts that were illustrated by each one. Preferred ones are identified, and the reasons for those preferences are also presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".