Assessing Process Skills in Engineering Students
Bibliographic record
Abstract
Assessing process skills in an undergraduateengineering program is an important and complex issue.Attributes like teamwork, ethics and professionalism aresubjective skills that are difficult to accurately assess. AtMemorial University’s Faculty of Engineering andApplied Science (FEAS), these skills are developed andassessed in ENGI 7102, The Engineering Profession. Thecourse uses one-on-one interviews, small groupdiscussions, a close connection to capstone design workand a blended approach to theory that allows for face-tofaceactivities and assessment. This paper will describethe methodology for the course development andinstructional design, along with a discussion of theactivities and assessments that capture process skillattributes for evaluation. In addition, ENGI 7102’s role inassessing graduate attributes for accreditation will behighlighted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".