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Record W4251210246 · doi:10.18260/1-2--34561

Engineering Students' Comprehension of Phase Diagram Concepts: An International Sample

2020· article· en· W4251210246 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
Fundersnot available
KeywordsMathematics educationCurriculumComprehensionCategorizationComputer scienceAerospaceSample (material)Phase (matter)Engineering ethicsEngineeringPsychologyPedagogyArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.311
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it