A Knowledge Building-Modeling Approach to Scientific Inquiry
Bibliographic record
Abstract
This study explores how a Knowledge Building -Modeling approach (KBM) was used to enhance grade 5 students' understanding of dynamic mechanisms related to natural hazards.Forty-three students collectively engaged in an iterative KBM process involving ideadriven discourse on Knowledge Forum.Additionally, students created models in response to emerging theories and questions.Iterations were driven by students' discourse and supported by learning analytics to advance idea and model building.Results show that students engaged in deeper discourse practices over time.Their models increasingly reflected complex causal reasoning and their knowledge of natural hazards improved.Analysis revealed that knowledge building discourse was a good predictor of both scientific understanding and modeling practices.We discuss the work that informed our intervention and highlight the KBM approach.Implications of designing knowledge building environments enriched with modeling to promote complex reasoning and modeling practices are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.026 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".