Comprendre les textes dans différentes disciplines : des défis à relever pour des étudiants de l’enseignement pré-universitaire et technique
Bibliographic record
Abstract
Dans le réseau collégial québécois (enseignement pré-universitaire et technique), de plus en plus d’étudiants présentent des difficultés à comprendre des textes complexes, et même des textes jugés simples par les enseignants. De plus, nombreux sont ceux, admis au collégial, qui présentent des besoins spéciaux liés à des troubles d’apprentissage et qui éprouvent des difficultés encore plus grandes. Cette recherche qualitative, premier jalon d’un projet de recherche-action formation, réalisée auprès de 16 enseignants et 11 étudiants provenant de différents programmes, nous renseigne sur la nature des difficultés de compréhension des textes disciplinaires faisant partie de leurs programmes d’études. Ces résultats plaident en faveur d’une formation continue des enseignants à des approches favorisant la compréhension de textes dans toutes les disciplines.
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 | 0.055 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.052 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| 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".