A cognitive apprenticeship approach to engineering education: the role of learning styles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it