The Creation of Virtual and Face-to-Face Learning Communities: An International Collaboration Experience
Bibliographic record
Abstract
This article examines the use of technology in higher education to support an international collaboration between 2 graduate seminars in cognition and instruction, one in Mexico and another in Canada. The culture of both seminars is described in the context of using computer mediated collaboration systems. The online collaboration between and within the 2 groups happened through the use of the communications tools available in WebCT, a Web-based course management system. The analyses reveal the discursive patterns between instructors and students in both settings, with an examination of teacher presence as it pertains to a cognitive apprenticeship perspective, with particular attention to teacher's modeling and scaffolding. We also present the nature of the student interactions in terms of the cognitive elements present in the discourse and the types of social interactions that support the community of inquiry model. Students in both seminars revealed high levels of critical thinking in the types of discussions they engaged in and the types of questions they posed to others. Differences were noted in the types of teacher modeling in the 2 seminars. These differences are explored and future directions are stated for promoting international collaborations in higher education.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".