Deliberative collaboration in learning-by-designing multimodal modeling activities
Bibliographic record
Abstract
Collaboration is often emphasized as one of the key twenty-first century competencies to promote scientific literacies through many representational modes. However, collaborative interactions have been often characterized as a coordinated, synchronous, and symmetrical activity in terms of the same level of knowledge with little attention paid towards addressing deliberative inquiry and its eclectic nature through multimodal resources. This paper aims to revisit the notion of deliberative collaboration by revisiting Dewey’s curriculum theories. As part of a series of design-based research, this qualitative case study reports collaborative learning processes among a group of five Singapore astronomy amateurs with the facilitator in a multimodal modeling workshop. Through the lens of Cultural-Historical Activity Theory, two contradictions were defined as a driving force to co-construct their conceptual understanding of distance and size of celestial objects and co-design multimodal models. This paper concludes with implications for supporting deliberative collaboration in scientific literacies.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".