Évaluer les apprentissages des étudiants à distance : explorations des \npratiques dans une université unimodale à distance
Bibliographic record
Abstract
Pendant l’année 2020, la pandémie de la COVID-19 a propulsé la formation à distance à l’avant-scène comme une solution « temporaire » ou « alternative » à la formation en présentiel, traditionnellement prédominante. Dans ce processus de mise à distance sans trop de préparation, our plusieurs enseignants, l’évaluation des apprentissages à distance s’est avéré un défi important. Pourtant, avant cette pandémie la formation à distance était en pleine essor, notamment au Québec et au Canada. Dans ce contexte, il nous est apparu intéressant d’explorer comment se déroule les évaluations dans les établissements qui œuvre en formation à distance depuis plusieurs années. Ainsi, dans le cadre de cette étude exploratoire, nous avons analyser les pratiques évaluatives (n = 458) au sein de 71 cours au sein d’un département d’une université unimodale à distance.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".