IMPLEMENTATION AND EVALUATION OF A COGNITIVE APPRENTICESHIP APPROACH TO CIVIL ENGINEERING
Bibliographic record
Abstract
From the onset of formal engineering education, engineering curricula have been based largely on science and mathematical knowledge. Applied subject based learning is a common teaching model in engineering education programs today. The professor 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. Furthermore, the engineering curriculum may neglect the critical skills that are necessary for a graduate student to be successful in the workplace, namely the reasoning and strategies that experts employ when they acquire knowledge or put it to work to solve complex real-life tasks (Collins et al., 1991). The purpose of this study is to design an optimal learning environment that meets the requirements of particular learning styles. We investigate a novel approach to teaching civil engineering, referred to as cognitive apprenticeship (Collins et al., 1991; Collins, 2006). The cognitive apprenticeship embeds learning in activities and makes deliberate use of the social and physical context. It tries to acculturate students into authentic practices through activity and social interactions in a way similar to that evident in craft apprenticeships. Collins et al. (1991) have developed a conceptual framework to design learning environments according to four principles regarding content, method, sequence and sociology. Traditional teaching practices do not sufficiently emphasize the reasoning and strategies that experts use to acquire knowledge and apply it to solve real-life problems (Collins et al., 1991), nor do they address students’ individual differences with regard to learning style preferences (Lowery, 2009). Therefore, the cognitive apprenticeship approach was implemented and evaluated to teach civil engineering and compared with a traditional teaching approach.Two experiments were conducted in order to compare the traditional and cognitive apprenticeship approach. The first was done with one group of students attending two different courses taught by the same professor. The first course was taught according to a traditional approach and the other, by a cognitive apprenticeship approach. The second experiment was conducted with a different group of students within the same course where one section of the course was taught according to a traditional approach and the other with a cognitive apprenticeship approach.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".