Reflective Practices in Engineering Education: A Personal Account of a Unique Experience Implementing a Dual Program
Bibliographic record
Abstract
The prevalent mindset in engineering education rests on a perception that the practice of engineering consists of acts of pragmatic and rational problem solving. Therefore, a positivist and knowledge centric paradigm to teaching guides many engineering educators. Nevertheless, many aspects of engineering can be viewed as subjective creative acts. This subjectivity implies that professional and personal culture will impact the practice of an engineer. Identifying and accounting for these subjective biases is therefore a responsibility of the engineer. Properly doing so is a skill that must be taught, learned and honed. Reflective practice is one of the many ways that can promote the development of this skill. In view of Dee Fink’s taxonomy of the Significant Learning, the reflective practice, implemented in conjunction with experiential learning opportunities, appears to be a powerful tool for learning to happen in engineering education. Reflective exercises have thus been introduced in several courses along the curriculum and a reflective journal is made as the backbone of the DUAL pathway of the Mechanical Engineering undergraduate degree at Université du Québec à Trois-Rivières’s Drummondville Campus. This paper describes the use of the Significant Learning Taxonomy and the reflective practice of engineering on the design of the experiential learning focused DUAL approach in a Mechanical Engineering undergraduate program and presents key insights about the exercise.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".