Inquiry-Based Learning with or without Facilitator Interactions.
Bibliographic record
Abstract
This paper discusses findings of a study investigating how students, on four online courses, engaged in inquiry-based learning with and without support from a facilitator. The investigation was conducted by analysing discussions of the online courses using the community of inquiry model. The results of the study imply that students in online discussions can engage in deep and meaningful learning, even when there is no facilitator interaction. Further the findings of the analysis suggest that successful inquiries are possible without teacher or facilitator interactions, if learning environments are designed to support students to be interactive and the students have motivation, regulatory skills and a willingness to collaborate with peers. Resume Dans le cadre de cet article, nous discutons les resultats obtenus dans une etude visant a determiner comment des eleves, participant a quatre cours en ligne, se sont engages dans un processus d’apprentissage par enquete, soit avec ou sans l’aide d’un facilitateur. L’etude a ete realisee en analysant les discussions relatives aux cours en ligne au moyen du modele du Community of Inquiry (CoI). Les resultats de l’etude laissent entendre que les eleves participant aux discussions en ligne peuvent realiser des apprentissages approfondis et significatifs, meme en l’absence d’interaction avec un facilitateur. De plus, les resultats de l’analyse suggerent que des enquetes peuvent etre reussies sans intervention de la part d’un enseignant ou d’un facilitateur si, d’une part, les environnements d’apprentissage sont concus de maniere a favoriser l’interactivite des eleves et, d’autre part, les etudiants ont la motivation, les competences requises et la volonte de collaborer avec leurs pairs.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".