Mechanical Engineering Capstone Design Course – CEAB Accreditation Outcomes Assessment
Bibliographic record
Abstract
Due to the comprehensive nature of the two-semester Capstone Design course for fourth-year students, many of the Canadian Engineering Accreditation Board (CEAB) Graduate Attributes (GrAtts), and associated learning outcomes (LOs), are present within the course. As the term ‘graduate attribute’ implies, students should exhibit specific attributes upon graduation. Thus, assessing these attributes in the students’ final year enables the faculty and students to verify that the goal of achieving these attributes has been accomplished. However, it is also necessary to examine the results of the assessments to make any downstream adjustments to the curriculum, to address any weaknesses in specific attributes.The undergraduate course calendar provides the following course description for the Capstone Design course:Student design teams, operating within a "company" environment, utilize the broad range of their undergraduate experience in interdisciplinary projects selected to promote interaction between the mechanical, automotive, and materials programs. Design methodologies and team interaction simulate future professional practice. Project milestones include: a design proposal with cost analysis and scheduling, construction and commissioning of the designed apparatus, and a final report and presentation having both global and detail completeness [1].One can see that the course is comprehensive; however, it is also quite unique for each design team as it pertains to each specific project. The uniqueness of projects, along with the fact that there are multiple advisors and assessors of projects, provides challenges to ensuring accurate and consistent assessments.This paper will describe what the department of Mechanical, Automotive & Materials Engineering (MAME) at the University of Windsor has incorporated into the Capstone Design course to assess many of the CEAB GrAtts and LOs, and implement a continuous improvement program for the department.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".