Enhancements To An Undergraduate Mechanisms Course
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1520 Enhancements to an Undergraduate Mechanisms Course W. L. Cleghorn, N. Dechev Mechanical and Industrial Engineering Department University of Toronto Introduction This paper covers techniques used at the University of Toronto to enhance the presentation of materials for an undergraduate mechanisms course. Some are based on the implementation visual aids in lecture presentations. The aids include the use of (i) translucent plastic models suitable for display on an overhead projector, (ii) physical models suitable for demonstration using a document camera and video projector, and (iii) computer animations which may be shown using a video projector. Examples are provided for each. The paper includes discussions of the relative merits and limitations of each of these methods of providing demonstrations, and means that the authors have employed to maximize their impact. In addition, the paper discusses the results of implementing a laboratory based on the Working Model 2D software. Classroom Demonstrations For several years the authors have been involved in the teaching of mechanisms courses. At one time they relied solely on translucent plastic models with an overhead projector to illustrate machine motions1. The models included linkages, meshing of gears and gear trains. These models are limited to the demonstration of planar systems. More recently, computer software packages have been available. They permit animations of more complicated spatial systems to be developed and illustrated2,3,4. Many lecture rooms at the University of Toronto, Faculty of Applied Science and Engineering are now equipped with a video projector and a document camera. The authors have used this equipment to advantage in the teaching of an undergraduate mechanisms course. Both physical models and computer animations are shown to students using this equipment, which complement the theoretical lecture material. At least one animation or model demonstration is shown during each lecture. They are shown to stimulate student interest and improve their understanding. “Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright 2003, American Society for Engineering Education”
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.371 | 0.147 |
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".