Engineering Students' Comprehension of Phase Diagram Concepts: An International Sample
Bibliographic record
Abstract
Abstract Materials science is an essential discipline for students in the mechanical and metallurgical engineering programs because many of them find jobs in industries where materials are relevant, such as electronics, aerospace, and automobile. Phase diagrams have proven to be a topic in materials science in which students demonstrate alternate conceptions. An essential first step in constructing a pedagogical approach to teaching phase diagrams in a specific program is to assess the students' conceptions. There has been significant interest in improving the teaching of materials science in general and phase diagrams in particular in two top universities, one in Mexico and the other in Canada. In both universities, there are successful mechanical engineering programs in which materials science is part of the curricula. In this research, we implemented a project aimed to improve the students' conceptions of crucial concepts in materials science. In this work, as a first step, we used an instrument inspired by items from the Materials Science Concept Evaluation (MSCE) to assess students' understanding of concepts related to phase diagrams. In addition to multiple-choice questions, we asked for their reasoning to deepen our understanding of their conceptions. We added open-ended items with corresponding spaces for their reasoning. We administered that instrument to undergraduate engineering students from these two universities after the phase diagram topics were covered. With the analysis of the multiple-choice and open-ended questions combined with a qualitative method to categorize the students' approach to each item, we present in this paper the students' conceptions and difficulties they had with this topic. We concluded that students in both countries had difficulties with the identification of phase fractions, the compositions of both alloys and individual phases, and solid solubility in binary phase diagrams.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".