Quantifying Student Competency Development Using the uOCompetencies Proficiency Survey
Bibliographic record
Abstract
This report provides a comprehensive evaluation of the uOCompetencies Proficiency Survey's role within the University of Ottawa's CO-OP program. It examines the survey's twin objectives: elevating the quality of feedback and evaluations, and precisely measuring student competency development. Initially focusing on the Fall 2023 cohort of 1017 students, this updated analysis now includes the Winter 2024 cohort, comprising 1043 students, and the Summer 2024 cohort, comprising 1799 students. Through the analysis of data derived from pre and post-surveys—filled out independently by students and their internship supervisors—and processed via the CO-OP Navigator and PowerBI, the study uncovers significant improvements. These include a positive shift along the competency proficiency scale, a notable alignment in assessment perceptions following consensus building between students and their supervisors during mid-term evaluations, and an increased level of student satisfaction with the feedback process. Notably, these results are consistently observed across all three semesters, despite variations in student profiles such as grade level and experience. The uOCompetencies Proficiency Survey is highlighted as a practical and effective tool for enhancing the quality of supervisor feedback, thus improving the student’s experience and abilities to refine, plan and implement their competency development objectives.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".