Soutenir la persévérance des étudiants (sur campus et à distance) dans leur première session d’études universitaires : constats de recherche et recommandations
Bibliographic record
Abstract
Quelles sont les mesures mises en place pour contrer le phénomène d'abandon des études universitaires?Comment des outils d'aide et de soutien à la persévérance aux études, accessibles en ligne, sont-ils utilisés par des étudiants nouvellement inscrits en première session d'études universitaires?Une étude auprès de 216 étudiants (sur campus et à distance) dans trois universités québécoises montre qu'ils ont des difficultés sur le plan des compétences et connaissances préalables, des stratégies d'apprentissage et d'autorégulation ainsi que de la lecture de l'anglais et du français, et que les mesures de soutien mises en ligne (S@MI-Persévérance) ont été utilisées pour résoudre ces difficultés .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".