Beyond Learning Management Systems: Designing for Interprofessional Knowledge Building in the Health Sciences
Bibliographic record
Abstract
This paper examines theoretical, pedagogical, and technological differences between two technologies that have been used in undergraduate interprofessional health sciences at the University of Toronto. One, a learning management system, WebCT 2.0, supports online coursework. The other, a Knowledge Building environment, Knowledge Forum 2.0, supports the collaborative work of knowledge-creating communities. Seventy students from six health science programs (Dentistry, Medicine, Nursing, Occupational Therapy, Pharmacy and Physical Therapy) participated online in a 5-day initiative to advance understanding of core principles and professional roles in pain assessment and management. Knowledge Forum functioned well as a learning management system but to preserve comparability between the two technologies its full resources were not brought into play. In this paper we examine three distinctive affordances of Knowledge Forum that have implications for health sciences education: (1) supports for Knowledge Building discourse as distinct from standard threaded discourse; (2) integration of sociocognitive functions as distinct from an assortment of separate tools; and (3) resources for multidimensional social and cognitive assessment that go beyond common participation indicators and instructor-designed quizzes and analyses. We argue that these design characteristics have the potential to open educational pathways that traditional learning management systems leave closed.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".