Teaching a Graduate Program Using Computer-mediated Conferencing Software1 : Distance Education Futures
Bibliographic record
Abstract
This article summarizes the authors' experiences developing and teaching the first two computer-mediated courses in an innovative Master of Continuing Education program at The University of Calgary in Alberta, Canada. It presents background information on this graduate program and its first student group, a brief description of the course development process and the teaching methods used, and more detailed discussion of issues that arose in the delivery phase. The article concludes with the authors' reflections on their experiences as they relate to the benefits and challenges of computer-mediated instruction cited in the literature and their recommendations for building successful computer-mediated learning environments. Cet article résume le travail des auteurs qui ont conçu et donné les deux premiers cours informatisés dans le cadre d'un programme novateur de maîtrise en éducation permanente, le Master of Continuing Education program, à la University of Calgary en Alberta, au Canada. On y trouve des renseignements sur ce programme de maîtrise et sur les premiers étudiants qui en ont fait partie, une courte description du travail d'élaboration du cours et de la méthode d'enseignement utilisée. L'article explique aussi les problèmes survenus pendant le déroulement du programme. En conclusion, il présente les réflexions des auteurs sur leur expérience, notamment sur les avantages et les défis de l'enseignement informatisé mentionnés dans le document, et leurs recommandations sur les milieux qu'ils jugent les plus propices à l'enseignement informatisé.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".