PrePrint: Proceeding from E-Learn 2005, Vancouver, 2005. Simulating Face to Face Collaboration for Interactive Learning Systems
Bibliographic record
Abstract
Abstract: The use of Problem-Based Learning (PBL) in medical education and other educational settings has escalated. PBL's strength in learning is mostly due to its collaborative and open-ended problem solving approach. Traditional PBL was designed to be used in live team environments rather than in an online setting. We describe research that allows for web-based PBL via geographically distributed physical locations that emphasize PBL's collaboration and open brainstorming approach using interactive web, gaming and simulation techniques. We describe Interactive Face Animation- Comprehensive Environment (iFACE) which allows for expressive voice based character agents along with Collaborative Online Multimedia Problem-based Simulation Software (COMPS) which integrates iFace within a customizable web-based collaboration system. COMPS creates an XML-based multimedia communication medium that is effective for group based case presentations, discussions and other PBL activities. We also discuss our medical school prototype that allows for brainstorming sessions, remote instructors and simulated patients. Figure 1: Instructor remotely delivering web based collaborative content in COMPS while controlling the expressive iFACE face agent via speaking through his computer microphone in real-time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.445 | 0.158 |
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".