REDESIGNING THE UBC FIRST YEAR INTRODUCTION TO ENGINEERING: SUCCESSES AND CHALLENGES
Bibliographic record
Abstract
A new first year introduction to engineering experience was developed at the University of British Columbia. This paper provides an overview of the two new courses and the lessons learned both in developing and delivering the courses. Several key problematic areas in the previous curriculum were addressed, namely, to improve student connection with the engineering profession, increase design and practical engineering experiences, more effectively integrate sustainability into the curriculum, and better emphasize the human and social connection to engineering.The courses operate in a flexible learning framework with a sequence of online, lecture, and studio components arranged in a whole-part-whole format delivered to a class of 850 students. Elements of numerous effective course design, teaching and learning practices, including integrated course design, constructive alignment, components of Team-Based Learning, classroom assessment techniques, peer evaluation, and peer grading were incorporated into these courses. Student feedbackthrough surveys has shown that the new format has been highly successful in addressing most of the key high-level goals, such as establishing a student connection to the engineering profession, helping students understand what engineers do and how they do it, and providing an introduction and appreciation for design, sustainability, decision-making, professionalism, and ethics..
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".