Scaffolding wiki‐supported collaborative learning for small‐group projects and whole‐class collaborative knowledge building
Bibliographic record
Abstract
Abstract While educators value wikis' potential, wikis may fail to support collaborative constructive learning without careful scaffolding. This article proposes literature‐based instructional methods, revised based on two expert instructors' input, presents the collected empirical evidence on the effects of these methods and proposes directions for future refinements. The instructional methods were implemented by an expert instructor teaching a 12‐week 68‐student undergraduate design class in Canada. Data were collected from observations, interviews and content analysis of wikis. The findings revealed that in small‐group project (SGP), the wiki instructional methods enhanced collaborative learning with most instructional methods derived from cooperative learning, but in whole‐class collaborative knowledge building (CKB), the wiki instructional mehtods failed to turn the class into a self‐sustained learning community after the scaffolding faded. We conclude that the genre of wikis should be different for SGP and CKB. While the students easily adopted the ‘reproduced’ genre of wikis for SGP with familiar tasks, they felt overwhelmed or resistant to the unfamiliar ‘emergent’ genre of wikis for CKB in massive collaborative constructive learning. Therefore, we propose that future refinements for wiki‐supported CKB should focus on providing students scaffolding for intersubjectivity (understanding collaborative constructive learning) and transfer of responsibility (developing autonomy).
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".