Preliminary Results from a High-Resolution Analysis of Accreditation Data to Assess Indicators, Identify Predictors and Assess Equitability of Teaching Practices
Bibliographic record
Abstract
The Department of Chemical and Biological Engineering (CHBE) at UBC has undertaken a high-resolution analysis of its accreditation data with the aim to identify any correlations between students’ declared gender, visa status (international or domestic), performance in individual indicators, course grades, and/or overall program GPAs. The hope is to extract meaningful information about the value of the indicators used in collecting information about our programs, increase the reliability of the data collected by reviewing those indicators that do not yield actionable data, identify predictors of student performance, and identify biases, if any, in the success rates of our students. This paper presents the results of the first steps of this analysis, using four core courses spanning CHBE’s programs. Correlations between performance in different indicators is represented by interaction heat maps and scatterplots, and performance by gender or student status are represented as violin plots. This paper serves as a proof of concept for this type of analysis, highlighting the value of this high-resolution look at accreditation data.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".