HOW FIRST YEAR ENGINEERING STUDENTS SELECT THEIR SPECIALIZATION AND HOW WE CAN BETTER SUPPORT THEM
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

 Abstract This paper explores the student experience of discipline selection, through the perspective of students in a common first year engineering program at the University of British Columbia. It also presents and examines a number of new innovations have been introduced to the UBC curriculum to support students in this regard. In general, there is limited information in the literature about how and when engineering students decide on their specific engineering discipline. What seems to be clear though is that many, if not most, students come into common first year engineering programs with a good idea (if not a decision) of what their specialization will be. In addition, short-term factors (such as courses and program experiences) dominate decision-making rather than long-term factors (such as career potential). The innovations we have introduced include program introduction videos, various online tools and resources, coordinated in-class presentations, program fairs, and more. Through a number of surveys to different cohorts of engineering students at UBC, several clear and encouraging trends have emerged. Most of our students report feeling well-prepared to choose their discipline by the end of first year; most students are not choosing their discipline until Term 2, after they have received information and presentations from all programs (having this time to gather information and decide is a key motivation behind a common first year); and most students report finding the new resources we are providing (online materials and tools, videos, Program Fairs, etc.) useful in their decision-making. Consistent with the literature, short-term considerations appear to dominate our students’ decision-making, although there are indications that longer-term career considerations are also starting to influence their information gathering. Having opportunities to speak to current and former students in a discipline was cited by our students as the most important information source in their decision-making. Also important was information provided by programs, both within our coordinated introduction to engineering course, and through websites and other program materials.
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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 it