AWARENESS OF SELF AND THE ENGINEERING FIELD: STUDENT MOTIVATION, ASSESSMENT OF ‘FIT’ AND PREPAREDNESS FOR ENGINEERING EDUCATION
Bibliographic record
Abstract
Understanding factors that influence incoming students’ preparedness and success is critical in improving educational efficacy. Students’ prior experiences, assumptions, and habits influence their engagement in process of learning to become competent design engineers. A thematic analysis of students’ reasons for pursuing an engineering major revealed such decisions to be based on self-assessed personal fit. This paper indicates four common types of personal fit as described by students: matching skillsets, desirable activities, meaningful impact, and exploratory intrigue. From these, two key factors emerged: an awareness of self (ie. skills, interests, values) and an awareness of the engineering field (ie. nature of its work, its value to society, its value to the individual). These factors were influenced by: prior academic performance in core courses, authoritarian influence and the presence of engineers within their social networks. The paper also discusses incoming students’ perception of design engineering attributes as revealed in their survey responses. We argue that efforts are needed to provide students, before and during university, with opportunities to engage with career engineers or engineering exercises in order for them to be able to accurately establish an understanding of the engineering field, negotiate expected learning outcomes, master effective strategies to succeed, assess their strengths and limitations. The data are drawn from a larger study on student motivation and learning process in design engineering education.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".