Developing a Reusable Resource for Teaching Task Analysis
Bibliographic record
Abstract
Many of the skills required in the practice of human factors are “process-oriented” rather than “product-oriented”; this is challenging for human factors educators because it is often more difficult to teach process-oriented skills and concepts than product-oriented skills and concepts. The Centre for Learning and Teaching Through Technology [LT3] at the University of Waterloo, Ontario, Canada is involved in developing learning objects (also known as “learnware”) to help faculty overcome instructional challenges associated with teaching processes. The learnware development model used by LT3, which enables learner-centered resources to be developed, for students, and by students, to teach about processes in various disciplines will be discussed. The Task Analysis learning object developed to overcome instructional challenges experienced by human factors professors in the department of Systems Design Engineering, will illustrate LT3's learnware development model. The development of Task Analysis learnware emphasizes how the application of iterative learner-centred design can aid in teaching fundamental concepts in human factors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".