Effects of Course Sequence on Software Programming in First-Year Engineering Open-Ended Projects
Bibliographic record
Abstract
In a large first-year introduction to engineering design course at the University of British Columbia students complete a five-week electromechanical design and build project at the beginning of term 2. An introduction to computing course, core to the program, may be taken either in the prior term or the same term as the introduction to engineering design course. In this paper the effects of completion of the computing course prior to commencing the project on team performance is analyzed. The data shows that the number of students with computing experience has only a modest impact on team performance for deliverables involving the description of the team’s application of the engineering design process such as poster presentations, and that teams with no students with computing experience generally outperform teams with one or two students with computing experience on the technical deliverable of the project. Possible causes for this finding, along with a summary of resources and teaching strategies aimed at supporting all teams in developing programming skills are presented.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".