A Decision-Making Framework for Engineering Mathematics Education
Bibliographic record
Abstract
Engineering is a fascinating blend of mathematics, science, and creativity, which helps to solve some of the world’s leading problems. This paper asserts that mathematics is central to engineering because it is the primary means by which engineers are able to model systems, as well as understand the consequences of their intended actions, designs, and operations. Thus, it follows that educators inherit an ethical responsibility to elucidate the importance of mathematics as a central theme to interventions in the world in general and to engineering in particular. This paper provides a strong theoretical overview of the existing and potential innovations that can be made to traditional undergraduate engineering mathematics instructions and further discusses the perceived gaps in the literature. A framework is proposed for teaching engineering mathematics: mathematics is the primary means by which ethical, responsible decisions can, and should, be made and it is thus crucially important that it be appreciated and taught as such within engineering programs. Keywords: Mathematics, Engineering, Decision-Making
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".