Supporting the Transition Between Mathematics and Physics in the First Year of University
Bibliographic record
Abstract
Abstract Undergraduate science students face difficulties using mathematics in their physics courses. Choosing an institutional perspective, we consider that these students experience a permanent transition between mathematics in their mathematics courses and mathematics in their physics courses. We refer to the anthropological theory of the didactic and the notion of didactic contract to understand this transition. In France, the Maths4sciences digital resources have been designed to help students learn the mathematics used in physics. We investigate students’ difficulties in the math-physics transition and the affordances and limitations of Maths4sciences resources to help them. We designed a physics exercise where students must recognize and solve a first-order linear differential equation. We interviewed three students who worked on this exercise and had access to a Maths4sciences tutorial sheet concerning such differential equations in a physics context. Through the analysis of these interviews, we observed that students faced different types of difficulties: recognizing mathematical types of tasks intervening in the technique for solving the physics exercise, performing types of tasks blending mathematics and physics, and making sense of physical notations, in particular. The Maths4sciences tutorial sheet only helped them with some of these difficulties. Beyond the cases studied, our work evidences the difficulties raised for students by different kinds of “recognition” types of tasks in physics and suggests directions for curriculum design and teaching mathematics for physics.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".