IMPROVING ENGINEERING EDUCATION: TWO KEY AREAS TO FOCUS OUR ATTENTION
Bibliographic record
Abstract
Engineering remains one of the most traditional and didactic disciplines in higher education. There is low adoption of research-based instructional practices with many educators believing adherence to tried-and-true methods in undergraduate engineering programs outweigh the benefits any change to more active learning could bring. Surveys of student engagement consistently rank the effectiveness of the undergraduate engineering experience lowest among the disciplines, with classroom observations confirming that engineering educators score significantly lower in delivery, teaching, lesson elements, and diversity. This quantitative study sets out to determine in which, if any, specific areas engineering educators score differently than their colleagues in other disciplines. Using Draeger and his team’s model of academic rigour as a framework, this study examines institutional data collected during three years of mandatory teaching observations of new full-time and randomly selected part time educators. The analysis shows that four key areas differentiate the teaching practices of engineering educators from their colleagues in other disciplines: (1) welcoming students, (2) explaining the lesson’s agenda, (3) the organization, pace, and planning of classes, and (4) the way material is presented to students. It is proposed that the undergraduate engineering experience can be improved by making changes to lesson structure, and enhanced by including opportunities for meaningful active learning.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".