APPLYING METACOGNITIVE STRATEGIES TO TEACHING ENGINEERING INNOVATION, DESIGN, AND LEADERSHIP
Bibliographic record
Abstract
Abstract – To encourage innovation and positive team behavior, a bonus innovation assignment is included at the start of the introductory design course. Students are encouraged to choose from a reading list and insert themselves in the material to explore how leadership, creativity, and innovation might impact their design team experience. Students are then introduced to CATME and asked to evaluate themselves and their team members on a monthly basis as they work on lab assignments and project work in a cooperative learning environment. Capstone and introductory design students assess their individual skills relative to the Canadian Engineering Accreditation Board (CEAB) graduate attributes (GA) pre and post course, including teamwork skills. In addition, capstone student design teams use reflection to self-assess team function based on their perceived attainment of team level, and confidence in their ability to perform categorized skills related to team performance, technical performance, planning and logistics performance. The goals of these changes are to provide a collaborative framework for students to construct activities to learn and develop innovation, team, and leadership skills. This report focuses on the structure of the cooperative learning framework and the development of five cooperative learning criteria: positive interdependence, individual accountability, face-to-face interaction, appropriate use of interpersonal skills, and regular assessment of group functioning. Assignment effectiveness is demonstrated.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".