Conception, implantation et utilisation d’un portfolio professionnel étudiant dans un programme de formation en ergothérapie
Bibliographic record
Abstract
Cet article présente les résultats d’une recherche-action sur la conception, l’implantation et l’utilisation d’un portfolio professionnel étudiant au Programme d’ergothérapie de l’Université d’Ottawa. L’objectif du projet était de concevoir un outil andragogique structuré qui permettrait à l’étudiant de développer et d’intégrer les éléments témoignant de ses apprentissages et de sa maturation professionnelle. Les constats des concepteurs et les commentaires des 70 étudiants ayant utilisé le portfolio pendant une année scolaire sont présentés. La tenue du portfolio s’est avérée pertinente et utile, mais une attention particulière doit être portée à son appropriation par l’étudiant. Quelques pistes de réflexion sont proposées. This paper presents the results of a Participatory Action Research on the design, implementation and ongoing use of a student professional portfolio at the University of Ottawa’s Occupational Therapy Program. The goal was to create an adult education tool that would allow students to develop and integrate all components of learning and professional growth. Feedback from the research team and 70 students with a one-year experience in using the portfolio indicates that the tool is relevant and useful, but special attention must be given to developing a greater sense of ownership on the students’ part. Avenues for reflection are offered.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".