Supporting digital competency development for vocational education student teachers in distance education
Bibliographic record
Abstract
Introduction In Quebec, aspiring vocational education teachers must enroll in a bachelor’s degree program in vocational education. At the Université du Québec à Rimouski, the Bachelor of Vocational Education (BVE) program is offered remotely and asynchronously in a digital learning environment. This project explores what digital competency resources are available to BVE students and the characteristics of the resources that students know, use and deem satisfactory. Methods This quantitative descriptive study was carried out in two phases. In the first phases, interviews and a literature search were used to identify the resources, which we analyzed according to the Analytical Framework of Resources Supporting Digital Competency Development and the Digital Competency Framework. In the second phase, 137 students evaluated 36 identified resources through a questionnaire. Results The findings reveal that the resources are not widely known, and even when known, they are infrequently used. However, when used, they are generally deemed satisfactory. Notably, resources are more frequently used when required for assessment in the introductory BVE course. Additionally, workshops are rated more satisfactory than videos. Discussion The results underscore the need for program instructors to actively promote these resources and suggest that further research is needed to better understand student needs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".