ENGINEERING PORTFOLIOS AT THE UNIVERSITY OF TORONTO: DEVELOPING SKILLS FOR COMMUNICATION,PROFESSIONALISM AND LIFE-LONG LEARNING
Bibliographic record
Abstract
In 2005, the University of Toronto’s Chemical Engineering and Applied Chemistry Department (CHE), in collaboration with the Engineering Communication Program (ECP), piloted a communications portfolio for second-year students. Over the past seven years the communication portfolio has been expanded into the third-year CHE requirements, adapted for use in the Mechanical and Industrial Engineering Department(MIE) and next year will be used within the Civil Engineering Department. Through a discussion of the CHE and MIE portfolios we compare two different portfolio models and explain how this model has been adapted to its newest context in Civil Engineering at the University of Toronto. Through this approach we aim to show the usefulness of this portfolio model in supporting student development in communication, professionalism and life-long learning, three of the CEAB graduate attributes.
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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 | 0.000 |
| 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".