Benefits and Challenges of Technology-Enabled Learning using the Community of Inquiry Theoretical Framework
Bibliographic record
Abstract
The Introduction to Technology-Enabled Learning (TEL) MOOC was intended to engage teachers worldwide who work in any level of education and are interested in technology-enabled learning and open educational resources. This paper investigates the response by participants to the content presented in week one on the Community of Inquiry (CoI) model, in particular, the benefits and challenges of using the CoI framework with in the classroom, whether it is online, blended or face-to-face. Titre: Bénéfices et défis des technologies pour l’apprentissage selon le cadre théorique de la communauté d’apprentissage L’introduction du MOOC sur les technologies pour l’apprentissage visait à engager des enseignants du monde entier qui, quel que soit le niveau auquel ils enseignent, s’intéressent aux technologies pour l’apprentissage et aux ressources éducatives libres. Cet article traite de la réponse donnée par les participants au contenu présenté dans la semaine une, sur le modèle de la communauté d’apprentissage, en particulier les bénéfices et défis accompagnant l’usage du cadre de la communauté d’apprentissage dans la salle de classe, que cela soit en ligne, hybride ou face-à-face. Mots-clés: CLOMs, Communauté d’apprentissage, Technologies pour l’apprentissage
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".