Methods of Applying Machine Learning to Student Feedback Through Clustering and Sentiment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning is used to analyze student feedback in first-year engineering courses. This exploratory work builds on previous research at the University of Toronto, where a multi-year investigation used an online survey to collect quantitative and qualitative data from incoming first-year students. [1] (N ~1000)Sentiment analysis, a machine learning method, is used to investigate the relationship between hours of study outside of scheduled instructional hours and qualitative survey feedback sentiment. The results are visualized with chronological sentiment graphs, which contextualize the results in relation to key events during the school year.Large drops in sentiment were seen to occur during weeks with major assessments and deadlines. An inverse correlation between hours spent outside of class and feedback sentiment was also noticed
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it