Learning progressions in context: Tensions and insights from a semester‐long middle school modeling curriculum
Bibliographic record
Abstract
Abstract Schwarz and colleagues have proposed and refined a learning progression for modeling that provides a valuable template for envisioning increasingly sophisticated levels of modeling practice at an aggregate level (Fortus, Shwartz, & Rosenfeld, ; Schwarz et al., ; Schwarz, Reiser, Archer, Kenyon, & Fortus, ). Thinking about learning progressions for modeling, however, involves challenges in coordinating between aggregate arcs in the curriculum and individual student learning trajectories. First, individual student performance is often dependent on students’ epistemic aims and the nature of the conceptual and representational context. Second, approaches for longitudinally supporting students in modeling is a relatively nascent endeavor, although notable exemplars have been developed (e.g., IQWST). Third, research on the highest levels of the proposed progression is often hypothetical, because few students demonstrate high‐level modeling practices in typical classrooms. In response to these challenges, we conducted a semester‐long design‐based study of eighth graders engaging in diagrammatic, physical, and computational modeling. In this paper, we explore conceptual and representational contexts designed to support sophisticated modeling practices and beliefs, analyze the nature of high‐level performances achieved through these contexts, and suggest revisions to the articulation of the Schwarz and colleagues learning progression to increase its utility and generalizability when viewed through a resource‐related lens.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".