Contrer l'abandon en formation à distance: expérimentation d'un programme d'accueil aux nouveaux étudiants à la Télé-université
Bibliographic record
Abstract
This article presents an overview of the sources used in setting up a program welcoming new students to l'Universite du Quebec's Tele-universite. The aim of this program was to heighten the degree of perseverance for those registering in the fall of 1991 for three certificate programs with the Travail, economie et gestion module. The nature and results of the program are set out and the findings are then interpreted. The conclusions underline the complexity of non-completion scenarios and emphasize the difficulty for institutions to address these problems. Dans cet article, nous proposons une recension des travaux dont nous nous sommes inspires pour mettre au point un programme d'accueil aux nouveaux etudiants a la Tele-universite de l'Universite du Quebec. Ce programme visait a ameliorer la perseverance des nouveaux inscrits aux trois certificats du module Travail, economie et gestion a l'automne 1991. Nous decrivons la nature et les resultats de cette intervention avant d'en livrer une interpretation. Nos conclusions mettent en evidence la complexite du phenomene de l'abandon et la difficulte pour les etablissements de formation a distance d'intervenir sur des facteurs significatifs pouvant le contrer.
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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 | 0.045 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".