Curriculum design for social, cognitive and emotional engagement in Knowledge Building
Bibliographic record
Abstract
Abstract Knowledge Building has been advanced as a pedagogy of engaged learning where students identify as a community whose purpose is to advance their shared ideas. This approach, which has been studied for three decades (Scardamalia & Bereiter, in: K. Sawyer (ed) Cambridge handbook of the learning sciences, Cambridge University Press, 2014), includes cognitive, social constructivist, and emotional elements (Zhu et al. in User Modeling and User-Adapted Interaction, 29: 789–820, 2019b). This paper investigates how refining Knowledge Building activities based on students’ feedback impacts their social, cognitive, and emotional engagement. Using a design-based research method, we refined successive course activities based on feedback from 23 Masters of Education students. With successive iterations, we found that the density of students’ reading networks increased; they theorized more deeply, introduced more authoritative resources, and made greater efforts to integrate ideas within the community knowledge base. As well, their level of negative affect decreased. These findings suggest that soliciting students’ input into course design can benefit their engagement and disposition toward learning, with implications for curriculum design.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".