Board 294: First-Year Engineering Students’ Desired Practices in Mechanical Engineering
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".