Teaching and learning about community development online: Insights and lessons learned
Bibliographic record
Abstract
This paper discusses the development, delivery, and evaluation of a successful graduate course in community development offered to students across Canada via the Internet. The review of literature points to common themes in distance education, community development and health promotion. The course, "Health Promotion: Community Development Approaches", is presented as a case example with descriptions of the curriculum, delivery methods, learning resources, activities and recommendations for future offerings of the course and for distance education in general. Although web-based distance education is challenging and requires instructors and learners to adapt, it can be an effective way to learn about concepts, and model the principles and ideology that are at the core of community development. Résumé : Le présent article aborde l’élaboration, la prestation et l’évaluation d’un cours de niveau supérieur traitant du développement communautaire offert aux étudiants de l’ensemble du Canada par Internet. L’examen de la documentation souligne les sujets communs dans l’éducation à distance, le développement communautaire et la promotion de la santé. Le cours « Health Promotion: Community Development Approaches » est présenté à titre d’exemple avec les descriptions du plan de cours, des modes de prestation, des ressources d’apprentissage, des activités et des recommandations pour les cours qui seront donnés ultérieurement et pour l’éducation à distance en général. Bien que l’éducation à distance effectuée au moyen du Web présente des défis et qu’elle exige des instructeurs et des apprenants qu’ils s’adaptent, il peut s’agir d’une méthode efficace pour en apprendre sur les concepts et pour présenter les principes et idées à la base du développement communautaire.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".