Web 2.0 and Social Media Connecting Learners in Self-Paced Study: Practitioners’ Perspectives | Le Web 2.0 et les médias sociaux reliant les apprenants dans l’étude à leur rythme : points de vue de praticiens
Bibliographic record
Abstract
Distance learners determine the time and place for their studies—those engaged in self-paced study may also choose the rate at which they proceed through their courses. However, it is difficult to incorporate purposeful learner-learner interaction into self-paced study. A multiple-case study included three open universities with in-house design and development of self-paced undergraduate courses. Data was gathered from in-depth interviews with course developers (academics and learning/teaching specialists), self-paced course materials, and institutional documents. This article reports on the ways in which these course designers and developers are making use of Web 2.0 and network-based approaches to encourage open, social forms of learner-learner interaction in self-paced courses. Les apprenants à distance déterminent à quel moment et à quel endroit ils étudient. Ceux qui étudient selon leur propre rythme peuvent aussi choisir la vitesse à laquelle ils progressent dans leurs cours. Il est toutefois difficile d’intégrer une interaction significative entre les apprenants dans les études adaptées au rythme de l’élève. Une étude de cas multiples a été menée auprès de trois universités ouvertes qui conçoivent et développent à l’interne des cours de premier cycle selon le rythme de l’apprenant. Des données ont été recueillies à partir d’entrevues approfondies avec les développeurs de cours (professeurs universitaires et spécialistes pédagogiques), du matériel des cours selon le rythme de l’apprenant et de documents provenant des établissements. Cet article rapporte les façons dont ces concepteurs et développeurs de cours utilisent le Web 2.0 et les approches en réseau pour favoriser des formes ouvertes et sociales d’interactions entre les apprenants dans les cours que ceux-ci suivent selon leur propre rythme.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".