Students’ Epistemic Commitments in a Heterogeneity-Seeking Modeling Curriculum
Bibliographic record
Abstract
Research about modeling emphasizes the importance of heterogeneity in science learning. At the same time, a growing body of scholarship seeks curricular pathways for epistemic and representational convergence. In response to this tension, we propose two constructs: heterogeneity-seeking curricula and commitments. Heterogeneity-seeking curricula emphasize generating and valuing multiple representations of phenomena, offering an image of science that foregrounds messy, nonlinear aspects of learning. Commitments parallel epistemic cognition research by focusing on values that shape students’ modeling; however, rather than looking for disciplinary practices in students’ modeling, commitments take students’ values as a starting point, mapping them to disciplinary resources not typically foregrounded in science education. Using a lens of commitments, we analyze six implementations of a heterogeneity-seeking 6th grade modeling curriculum, and we compare the lens of commitments to the lens of epistemic ideals. Then, we show that, in this context, commitments functioned like epistemic ideals by acting as evaluative resources during modeling. However, commitments also extended beyond this role by helping students ask and explore questions that were not anticipated by the curriculum, problematizing a view of phenomena as objective and external to students’ modeling work and showing them instead to be a production of the classroom’s multidimensional modeling discourse.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".