A Comparison of Participation Patterns in Selected Formal, Non-formal, and Informal Online Learning Environments / Comparaison des modes de participation dans des environnements formels, non formels et informels d'apprentissage en ligne
Bibliographic record
Abstract
Does learner participation vary depending on the learning context? Are there characteristic features of participation evident in formal, non-formal, and informal online learning environments? Six online learning environments were chosen as epitomes of formal, non-formal, and informal learning contexts and compared. Transcripts of online discussions were analyzed and compared employing Transcript Analysis Tools for measures of density, intensity, and reciprocity of participation (Fahy, Crawford, & Ally, 2001), and mean reply depth (Wiley, n.d.). This paper provides an initial description and comparison of participation patterns in a formal, non-formal, and informal learning environment, and discusses the significance of differences observed. La participation des apprenants varie-t-elle en fonction du contexte d'apprentissage? Existe-t-il des caractéristiques de participation spécifiques aux environnements formels, non formels et informels d'apprentissage en ligne? Six environnements d'apprentissage en ligne ont été sélectionnés pour illustrer les contextes formels, non formels et informels d'apprentissage et ont été comparés. Les transcriptions des discussions en ligne ont été analysées et comparées à l’aide des Transcript Analysis Tools pour mesurer la densité, l'intensité et la réciprocité de la participation (Fahy, Crawford, et Ally, 2001), ainsi que la profondeur moyenne de réponse (John Wiley & Sons, nd). Cet article décrit et compare les modes de participation dans un environnement formel, non formel et informel d'apprentissage, et discute la portée des différences observées.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".