USING MACHINE LEARNING AS A TOOL TO HELP GUIDE UNDECLARED/UNDECIDED FIRST-YEAR ENGINEERING STUDENTS TOWARDS A DISCIPLINE
Bibliographic record
Abstract
Supervised Machine Learning classification algorithms are used to analyze the potential inclination of the undecided/undeclared first-year engineering students. The data exploration task is possible by building a dataset that comprises of questions based on significant attributes. These attributes hover around different disciplines of engineering being offered at the University of Toronto. This qualitative survey is distributed to upperclassmen students (3rd, 4th year and graduate students, N = 54) and undecided first-year engineering students (N = 29) Multi-class classification is a technique that is used to categorize the data into two or more classes, in this case, the different disciplines of engineering at University of Toronto. The dataset that is built, based on the answers provided by the upperclassmen, is programmed into different classification algorithms such as Logistic Regression, KNN (K-nearest neighbors), Decision Tree and Random Forest classifier. The algorithms are compared so as to identify the most appropriate one that can determine the specific class label of the upperclassmen based on the answers provided in the qualitative survey. The accuracy of the various algorithms is an indicator of the favorable algorithm that can serve as a tool to suggest the potential majors that could be pursued by the undecided/undeclared students. Moreover, the answers given by the upperclassmen is visually analyzed for identifying the patterns of inclination of the students belonging to different disciplines of engineering.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".