A cognitive apprenticeship approach to engineering education: the role of learning styles
Bibliographic record
Abstract
Prior to the creation of engineering schools, engineering was taught in an apprenticeship style. However, from the onset of formal engineering education, engineering curricula have been based largely on science and mathematical knowledge. Applied subject based learning (usually called traditional teaching methods) is still a common teaching model in engineering education programmes today. The professor or tutor passes information to the students, the newly acquired knowledge is applied to specific problems and communication between students and professor (and between students themselves) is limited. In order to better prepare future engineers for the workplace, many engineering educators are implementing innovative approaches to teaching and learning in their classrooms (e.g. problem based learning). In the work described in this paper, a cognitive apprenticeship approach is used. This teaching model includes the main assumptions of the problem based learning approach and also defines instructional methods for enhancing learning. The model was used for teaching two groups of civil engineering students enrolled in their third and fourth year. Results of the two experiments showed that the cognitive apprenticeship approach used for teaching undergraduate civil engineering students was favoured by most of the students, independent of their preferred learning style. The implications of these findings with regard to implementing the cognitive apprenticeship approach in civil engineering education are discussed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".