Evaluating a learning progression for the solar system: Progress along gravity and dynamical properties dimensions
Bibliographic record
Abstract
Abstract We previously proposed a hypothetical learning progression around the disciplinary core idea of the Solar System and its formation as a first step in a research program to begin to fill this gap and address questions of student learning in this domain. In this study, we evaluate the effectiveness of two dimensions within the learning progression, dynamical properties and gravity, in describing change in how student reason in the domain across the course of their 14‐week astronomy unit. A sample of sixth‐grade students (N = 24) were interviewed before and after instruction. We compared changes in how students explained the dynamic properties of planets and the role of gravity in the Solar System to their experiences during instruction. Our findings provide evidence for the usefulness of this learning progression in describing how students' explanations may progress, offer insight into how instruction may support that progress, and highlight the challenges in drawing conclusions on how students' explanations may progress when limitations are identified in instructional experiences. We also discuss the connection between these two construct maps but also point out what appears to be a missing element in our original definition of the learning progression: inertia.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".