Data Analytics in Engineering Education: Lessons Learned from a Canadian Engineering School
Bibliographic record
Abstract
We extended our earlier student data analytics work [1] to further analysis of 10 years of 11,000+ engineering undergraduates’ academic records in the Faculty of Applied Science and Engineering at the University of Toronto to uncover underlying factors impacting students’ academic performance upon graduation. We explored the potential of using supervised and non-supervised machine learning algorithms to select the key features that strongly correlate with the identified students’ academic performance. K-means clustering method was used to study math competency on academic performance and the findings indicate factors other than math can significantly influence student performance. We also applied Association Rule to study life-long learning attributes based on the completion of minors and certificates. From the results, we noticed that technical minors choices correlated strongly with specific core engineering programs and there are also marked differences in terms of gender, legal status and Professional Engineering Year/COOP completion. Various visualization graphical methods including Heatmap, Pie Char, Sanky Diagrams were applied to aid the analysis. The power of data analysis enables a better understanding of our engineering students’ experiences and informs evidence-based decision-making in our school.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".