AWARENESS OF SELF AND THE ENGINEERING FIELD: STUDENT MOTIVATION, ASSESSMENT OF ‘FIT’ AND PREPAREDNESS FOR ENGINEERING EDUCATION
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.
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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it