The search for learning community in learner paced distance education: Or, 'Having your cake and eating it, too!'
Bibliographic record
Abstract
<span>University distance and e-learning programs generally follow one of two models. Most dual mode institutions and some open universities follow a model of cohort learning. Students start and terminate each course at the same time, and proceed at the same pace. This model allows for occasional or regular group based activities. The second model, referred to as learner paced, is based on increased student independence. Students may start their courses at many points during the year, and complete these at their own pace, depending on the learner's circumstances and interests. It is much more challenging to integrate group based activities in this learner paced model. This study is situated in a university that supports continuous intake and learner pacing in its undergraduate programs. Athabasca University is investigating the feasibility and effectiveness of adding collaborative and cooperative learning activities to this model. The report summarises a study of learner interactions in the context of learner paced courses delivered by the University. Following a review of relevant literature, the study reports on interviews with Athabasca University faculty and external distance education experts, describes results from an online survey of undergraduate students, and documents how these findings may be operationalised at the University. An extensible model of community based learning support is proposed to utilise new social computing capabilities of the web, and to permit learner-learner interaction in a scaleable and cost effective manner, while retaining learner pacing.</span>
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".