USE OF EPORTFOLIO TOOL FOR REFLECTION IN ENGINEERING DESIGN
Bibliographic record
Abstract
Electronic portfolios (ePortfolios) can be a beneficial tool to facilitate student learning, evaluate learning outcomes and showcase skills and experience. At the University of Guelph, the School of Engineering piloted the use of ePortfolios within the third year design course of the engineering design sequence of courses. With the implementation of graduate attributes by the CEAB, more “soft skill” attributes like individual and teamwork, project management, and lifelong learning are important skills developed by students within the design courses and can be assessed within an ePortfolio environment.Students submitted guided reflections related to major deliverables within the course. The reflections were assessed for the level of insight through rubrics in the learning management system. Overall, students improved their ability to reflect and provided good insight into their learning and roles within their group project. The response to the reflections by students was mixed. Many students found value in reflecting on their experience while other students were frustrated by the method of filling the reflection form.In the future, the objectives for reflection should be made clearer with supplementary documentation to the lecture material. Adjusting the timing of the reflections to correspond to less stressful periods of the semester and improving the ePortfolio process will help with student engagement.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".