USING MACHINE LEARNING AS A TOOL TO HELP GUIDE UNDECLARED/UNDECIDED FIRST-YEAR ENGINEERING STUDENTS TOWARDS A DISCIPLINE
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
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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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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