Fostering Authentic, Sustained, and Progressive Mathematical Knowledge-Building Activity in Computer Supported Collaborative Learning (CSCL) Communities
Bibliographic record
Abstract
Eliciting high-level mathematics symbolizing and communicating from students engaged in mathematics communities of practice has been found to be a challenging problem. In this article, we report on a study where 21 grade six female students engaged in model-eliciting problem-solving with collective discourse mediated by Knowledge Forum® Computer Supported Collaborative Learning (CSCL) software achieved the kind of progressive knowledge-building activity that until now had not been achieved in CSCL-mediated mathematics communities. During the course of the study, the students engaged in knowledge-building discourse about and iteratively improved their models for ranking the cities of Canada in terms of livability. The success achieved in having the students engage in this knowledge-building activity was attributed to the contexts provided by the model-eliciting math problem and to contexts and scaffolds for knowledge-building discourse provided by Knowledge Forum® during the construction and iterative revisions of the math models.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".