Scaffolding problem-based learning with CSCL tools
Bibliographic record
Abstract
Small-group medical problem-based learning (PBL) was a pioneering form of collaborative learning at the university level. It has traditionally been delivered in face-to-face text-based format. With the advancement of computer technology and progress in CSCL, educational researchers are now exploring how to design digitally-implemented scaffolding tools to facilitate medical PBL. The “deteriorating patient” (DP) role play was created as a medical simulation that extends traditional PBL and can be implemented digitally. We present a case study of classroom usage of the DP role play that examines teacher scaffolding of PBL under two conditions: using a traditional whiteboard (TW) and using an interactive whiteboard (IW). The introduction of the IW technology changed the way that the teacher scaffolded the learning. The IW showed the teacher all the information shared within the various subgroups of a class, broadening the basis for informed classroom scaffolding. The visual records of IW usage demonstrated what students understood and reduced the need to structure the task. This allowed more time for engaging students in challenging situations by increasing the complexity of the problem. Although appropriate scaffolding is still based on the teacher’s domain knowledge and pedagogy experience, technology can help by expanding the scaffolding choices that an instructor can make in a medical training context.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".