L’effet du tutorat individuel sur le sentiment d’auto-efficacité et la persévérance en formation à distance
Bibliographic record
Abstract
Issue des préoccupations concernant l’abandon en formation à distance, la présente recherche vise à explorer les effets de l’introduction de mesures de tutorat individuel sur le sentiment d’auto-efficacité et la persévérance des étudiants. Une méthodologie mixte a été utilisée, en recourant à une étude quasi-expérimentale menée auprès de 778 participants et à l’analyse d’entrevues individuelles téléphoniques. Les taux de persévérance ont été plus élevés dans le groupe expérimental où des mesures de tutorat individuel ont été introduites. L’analyse qualitative montre que les interventions des tuteurs des groupes expérimentaux ont été bien perçues et favorisent les contacts ultérieurs entre tuteurs et étudiants.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".