DEVELOPMENT OF PEER TEACHING SUPPORTED DESIGN FOR X MODULES FOR SENIOR ENGINEERING DESIGN PROJECTS
Bibliographic record
Abstract
Abstract – This paper presents initial results from a series of modules introducing Design for X (DfX) concepts to student project teams. DfX methods are recognised as a means to facilitate decision making throughout the design process and directly support the “Design” graduate attribute as defined by the Canandian Enginering Accreditation Board. In this senior design course, third and fourth year students are integrated into the same project teams, with the aim of promoting peer learning and leadership. Therefore, a flipped classroom approach was applied to the modules, followed by team-based reflection and discussion after which the appropriate DfX methods were to be applied within the projects. In the second semester third year students from each team were required to update the class on how these methods had been implemented in their projects. Preliminary results show active participation by the fourth year students, with varying levels of application of the tools within the projects. This paper will present specific examples of student led discussion and exercises surrounding DfX topics, as well as a qualitative evaluation of the application of DfX methods by the student teams. Recommendations for future improvement include the development of additional short duration, concrete, formative DfX activities for inclusion in the modules.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".