DEFINING THE APPROPRIATE COURSE PROJECT FOR FOSTERING THE EXPECTED COGNITIVE COMPETENCIES: EBD APPROACH TO AN ENGINEERING DESIGN COURSE
Bibliographic record
Abstract
Project-based learning is an inevitable part of current course curriculums, especially in engineering design courses. Incorporating course projects in curriculums is done for overcoming the lack of students’ familiarity with real-world challenges. Students either acquire or further develop those specific competencies upon successful completion of the course project. Thus, defining an appropriate course project becomes essential. The competencies that are fostered may depend either on the design problem or the project contexts. In this study, we employ an EBD approach to developing a framework for evaluating a course project regarding its fitness to course learning objectives. This framework makes it possible to elicit required competencies for accomplishing a course project and comparing it with the set of competencies in the course learning objectives. A case study of a flying house design project is presented to demonstrate the framework application. The discussion of the proposed framework and future directions to our research are presented at the end.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".