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Record W4391615072 · doi:10.18260/1-2--42800

Board 294: First-Year Engineering Students’ Desired Practices in Mechanical Engineering

2024· article· en· W4391615072 on OpenAlexaff
Jingfeng Wu, Shannon Clancy, Erika Mosyjowski, Shanna Daly, Lisa R. Lattuca, Joi‐Lynn Mondisa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Calgary
FundersNational Science Foundation
KeywordsTeamworkCreativityEngineering educationWork (physics)Field (mathematics)Variety (cybernetics)Engineering ethicsEngineeringEngineering managementComputer sciencePsychologyMechanical engineeringManagementArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Engineering requires comprehensive skills, including both technical and socio-technical skills. However, the engineering practices that are introduced in coursework tend to predominantly emphasize the technical skillsets. Research has shown that the perception of engineering as a technical-only field can alienate students who hold beliefs in communal goals, even though they achieve excellent academic performance in their engineering coursework (Stevens et al., 2008; Danielak et al., 2014). Such research findings point to the need for developing greater understanding of the types of skills and practices that could potentially invite students to particular disciplines within engineering. Thus, our research focuses on understanding the aspects of engineering practices that first year students describe as important to their reasons for pursuing mechanical engineering. To explore this question, we drew on a subset of data from our larger multi-methods study, analyzing data from in-depth interviews with four first-year students interested in pursuing mechanical engineering at a research-intensive university in North America .Through these semi-structured interviews, we focused on students' motivations for pursuing engineering, their first-year course experiences, and their own interests and goals in engineering. The findings revealed that the students felt motivated to pursue mechanical engineering to engage in various practices, including technical analysis, design work, societal impact, collaboration, and communication skills. The findings demonstrate diverse practices that drew different students to pursue engineering as a major and a career. Compared to their course experiences, students expressed interest in more focus on developing socio-technical and communication-based skills. These findings contribute to our understanding of how engineering courses can recognize and provide development opportunities for diverse engineering practices, ultimately supporting students in achieving their goals as engineers. Work from this project was funded by an NSF grant within the Division of Undergraduate Education (DUE) in the EHR Core Research (ECR) program.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.260
Teacher spread0.244 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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