Trois stratégies pour favoriser l’engagement des participants à un MOOC
Bibliographic record
Abstract
Les principaux défis des MOOCs se résument souvent au faible taux de réussite (voir Breslow et al., 2013; Gillani, 2013; Karsenti, 2013), aux questions de propriété intellectuelle des contenus de cours (voir EDUCAUSE, 2012; Fowler et Smith, 2013; Porter, 2013) et aux mécanismes de l’évaluation certificative (voir Cisel et Bruillard, 2012; Liss, 2013; Yuan et Powell, 2013). Dans le cadre de ce texte, nous nous intéressons tout particulièrement aux mesures de soutien au processus d’apprentissage (voir Karsenti, 2013; Kop, 2011; Kop, Fournier et Mak, 2011; Tschofen et Mackness, 2012), et tout particulièrement aux stratégies pouvant être mises en place pour favoriser la motivation des apprenants (voir Kizilcec et Schneider, 2015) et, ainsi, la poursuite du parcours des apprenants inscrits dans les MOOCs. À partir de la théorie de l’autodétermination de Ryan et Deci (2000), nous explorons comment les trois tendances caractéristiques retrouvées actuellement dans certaines formations à distance (l’apprentissage nomade, la ludification des activités d’apprentissage et l’apprentissage adaptatif) sont susceptibles d’accroître la motivation des apprenants qui participent à des MOOCs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".