STUDENT’S PERSPECTIVE ON IMPROVEMENTS FOR SECOND YEAR AND OTHER UNDERGRADUATE ENGINEERING DESIGN COURSES
Bibliographic record
Abstract
Students and educators should work together on pushing the boundaries of expectation - "expect more to get more" - so that design courses can become increasingly effective and greater potential can be achieved. I encourage instructors and educators to consider the fact that this generation of engineering students enter university with a multitude of experiences and skills in design, decision making and communication that past generations did not have – and taking advantage of that advanced starting point will greatly improve the caliber of the course offerings. As an engineering undergraduate student at Queen’s University, I have been exposed to several engineering design courses. Starting in the 2011-2012 academic year when I was in my second year, a course referred to as APSC 200/293 was introduced. I will be critically discussing my experiences both positive and negative with this design course, as well as the first year design course, and in my opinion how they can be improved. Information and feedback regarding the design courses was obtained from the faculty and compared to my observations. Starting the design courses early on in the students’ careers gives them an excellent introduction to the real world of engineering. However, there is great room for improvement in courses of this sort, which can be assisted by utilizing the opinions of willing students. I will further discuss these courses, their downfalls and advantages, and where improvements could be made to promote the success of students.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".