Bibliographic record
Abstract
RÉSUMÉ. Les nouvelles réalités mondiales nous placent dans un contexte d’enseignement/apprentissage hétérogène. La recrudescence des mouvements migratoires, la mobilité interuniversitaire, la conciliation études-travail-famille, par exemple, modifient le profil des classes qui sont dorénavant constituées d’étudiants aux niveaux de connaissances divers, aux parcours éducatifs variés, aux styles d’apprentissage différents. Quelles seraient les solutions didactiques pour gérer l’hétérogénéité en classe? Le mode hybride s’en avère une, avec sa flexibilité et son efficacité surtout pour les apprenants adultes. Hyperliens, vidéos, forums de discussion, collaborations en direct et différé sont quelques-uns des outils multimédias que nous utilisons dans notre cours mixte « Le français des affaires » de l’Université Concordia (Canada) et grâce auxquels les participants profitent de la variété des profils présents dans la classe. Ce mode comporte aussi des contraintes : accorder plus de temps tant pour le professeur que pour les étudiants, établir une complémentarité nécessaire entre séances en présentiel et à distance, posséder une dextérité technique. Mots-clés : apprentissage/enseignement, étudiant-acteur, formule hybride, hétérogénéité, TIC. ABSTRACT. The new global realities create a heterogeneous teaching/learning context. The resurgence of migratory movements, inter-university mobility, and the school-workfamily balance, for example, modify the composition of classes, which are henceforth made up of students with varying levels of knowledge, varied educational backgrounds, and different learning styles. What are the didactic solutions for managing heterogeneity in the classroom? The blended mode, with its flexibility and effectiveness especially for adult learners, is one of them. Hyperlinks, videos, discussion forums, online and offline collaborations are some of the multimedia tools we use in our “Business French” class at Concordia University (Canada), where participants benefit from the variety of student profiles. This mode also has constraints: the need to allow more time for both professor and students, the need to establish complementarity between face-to-face and online sessions, and the requirement of technical skills. Keywords: blended learning, heterogeneity, ICT, learning/teaching, student-actor.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".