L’évaluation des apprentissages à l’ère du numérique en enseignement supérieur : quels besoins et quels défis ?
Bibliographic record
Abstract
In Quebec, college-level higher education institutions with programs aiming to develop student competencies are increasingly turning to distance learning (DL) and to digital technology (DT) integration to make studies more accessible to a larger number of students. Based on observations that assessment practices incorporating digital technology are little known, this study sets out to identify the distance learning assessment practices used by teachers in a course context, as well as the needs and challenges associated with them. Qualitative/interpretive research conducted with college educational counsellors (n=16) and network representatives (n=2) has helped shed initial light on assessment practices that incorporate DT. The results of the first stage of the study highlight the emergence of distance learning assessment practices that are developing at varying rates, and attest to the needs and challenges involved at the macro (institution), meso (program) and micro (course) levels.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".