Measuring the Connection Between Mathematics and Engineering
Bibliographic record
Abstract
Mathematics forms the foundation for all the engineering disciplines. Students have trouble transferring this mathematical knowledge from their mathematics classes to the rest of their undergraduate engineering classes. This study is borne out of a desire to ‘be better,' to endeavour always to try to improve, but first, you need to know where one the starting point. The authors are also passionate about mathematics as it relates to engineering. Anecdotally the authors had heard that both students and faculty were disappointed and aggravated with the current status of mathematics teaching in undergraduate engineering. With no known study in Canada looking at how mathematics connects with engineering the authors went down the path to find out how strong the connection between mathematics and undergraduate engineering is at the University of Toronto.Through a mixed-method survey, the goal was to measure respondents’ (i.e. The teaching staff) views on the importance of and students’ competence of both mathematical topics and specific mathematic skills. A survey was administered in the 2017 fall semester to all of those who teach in the Faculty of Applied Science at the University of Toronto. The first part of the survey used a 5-point scale, the second part of the survey had open-ended questions.The responses to the 5-point scale questions demonstrate that the selected mathematic topics and specific skills were all seen as important and that the students’ competence was lower than their rated importance. The open ended-questions asked for respondents definitions and views as they related
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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 | 0.000 | 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 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".