Bibliographic record
Abstract
This chapter examines the concept of collaborative learning and its theoretical and practical foundations. Collaborative learning takes place in a structured social situation where a group of students work as a team to assist each other with learning tasks. The instructional strategies encourage student to student interactions. Drawing on group work skills, collaborative learning has been demonstrated to be effective in a variety of learning situations. Development of a variety of Internet technologies such as communication tools, emails, discussion forums, video and audio tools together with webcasting allow collaborative teaching strategies to be used creatively in online learning. The authors have trialed the use of various technologies in the human services and several case examples of online collaborative learning are provided. These case studies cover activities such as supervision and controversial issues in social work ethics. The chapter concludes with a discussion of the future directions and the challenges this poses for traditional classroom teaching.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.020 |
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".