Engineering education: Nurturing a holistic skill set for future success through an introductory course
Bibliographic record
Abstract
The choice of post-secondary courses is heavily influenced by individuals' perceptions of their chosen discipline. Freshmen often bring preconceived notions about engineering, impacting their course preferences. Students excelling in Physics and Mathematics typically gravitate towards engineering, believing it provides a competitive advantage. However, beyond technical skills, engineering demands proficiency in soft skills like problem-solving, teamwork, and creativity. This article, authored by teaching assistants of an introductory engineering design and communication course, delves into the holistic aspects of engineering. The course aims to provide a comprehensive understanding of engineering by emphasizing critical skills such as project management, communication, teamwork, adaptability, safety, and ethics. Despite the belief that a strong background in physics and mathematics suffices for engineering success, this course stresses the importance of a well-rounded skill set. The authors emphasize the necessity of developing non-technical skills for a successful engineering career, challenging the conventional narrative, and advocating for a more comprehensive approach to engineering education. The authors underscore the importance of students' perspectives on these concepts and provide their recommendations and a call to action to improve the student perception of such courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".