Bibliographic record
Abstract
In this paper I address one question asked by teachers who teach online-“How can I build community among my learners in my class? ” This paper provides an answer; in fact, it provides ten possible answers, in the form of ten models for teachers to use to build community in on-line courses. Each model has been tried and tested over ten years of post-secondary experience in designing and teaching twenty-nine online courses at four institutions in Canada. Community can be built in online courses. Each model offers ten unique approaches regarding how to develop community among learners and teachers in a course. The tacit notion hidden within and throughout each model is that courses that develop community and good pedagogic relationships among learners and teachers are those that are intentionally designed to do so. Each model described in this paper includes a unique structure of ideas, a rational for the model’s use and some strong theoretical support. Each model is a particular expression of the general concept of constructivism-that thinking is socially constructed, and knowledge a social construction. When intentionally designed to do so, an online class activity of socially constructing some project or collaborating
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.054 |
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".