DIVERSITY IN ENGINEERING UNDERGRADUATE EDUCATION: A CASE FOR NONCOGNITIVE VARIABLES IN ENGINEERING ADMISSIONS
Bibliographic record
Abstract
In this paper we investigate anoncognitive assessment tool that can be used inconjunction with traditional, cognitive approaches (e.g.,high-school averages) to undergraduate engineeringadmissions. The motivation for this work relates to theSchulich School of Engineering’s desire to attractstudents who bring a wide range of interest and abilitiesbased on cultural, race, gender or other aspects ofdiversity. Our hope is that a holistic approach toadmissions will provide us with a means to consider abroader, more diverse student population, while stillproviding good predictors of student success in first yearengineering.We describe a pilot study where all students student inthe Schulich School of Engineering’s first yearengineering design and communication course wereasked to complete the Noncognitive Questionnaire (NCQ)at the beginning of the Fall 2014 term. The results of thissurvey are then compared to overall student performanceat the end of the Fall 2014 term to gauge the correlationbetween NCQ scores and student performance. The resultof our study show that the NCQ is most useful for transferstudent admissions where the population is nonhomogeneous(i.e., arriving from a variety of institution,various age and experience levels), while average gradeis the best predictor of student success for high-schooladmissions.
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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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".