CURRICULUM ENHANCEMENT AND EVALUATION OF GRADUATE ATTRIBUTES AND OUTCOMES THROUGH STUDENT-RUN FORUMS
Bibliographic record
Abstract
The evolution of a curriculum involves changes at many different levels such as daily changes to reflect questions or areas of interest of a particular class, improvements to an established course based on observations from the professor, or more significant changes to streams of courses at a departmental level, or adaptation to suggested accreditation guidelines such the recent new Canadian Engineering Accreditation Board (CEAB) graduate attributes and outcomes. Most educational institutions have means of collecting data and assessing individual courses or streams of courses based on student performance, course evaluations, and professor assessments. However, since more can be done to gauge the collective effect of changes before students get to their final year capstone project or go into industry, a student-run curriculum forum has been established.This paper presents some of the lessons learned from the bi-annual student-run curriculum forums in the Department of Electrical and Computer Engineering at the University of Manitoba. Based on the experience acquired so far, this paper outlines the organization of the curriculum forums, suggestions on guided discussions, ways to present feedback, and means of communicating to students how their feedback is being used to improve the curriculum.
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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 | 0.061 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".