Learning Analytics based on Bayesian Optimization of Support Vector Machines with Application to Student Success Prediction in Mathematics Course
Bibliographic record
Abstract
Learning analytics is receiving a growing attention from both machine learning and education communities, where support vector machines (SVM) are gaining popularity over existing data mining techniques. In the scope of this work, we employ SVM to predict student success in mathematics course in Portugal under two common nonlinear kernel functions: polynomial and radial basis function kernel. In addition, we employ the k-nearest-neighbor (kNN) algorithm as a reference model since it is known to be fast and effective in various classification problems. Furthermore, we adopt the Bayesian optimization (BO) technique in a cross-validation framework to optimize SVM key parameters; namely, the slack parameter and penalty coefficient. The obtained experimental results show that the SVM outperform k-nearest-neighbor algorithm under both nonlinear kernel functions. Additionally, processing time associated with SVM optimization process increases with polynomial order. Furthermore, the SVM trained with third-order polynomial kernel performs the best. Finally, k-nearest-neighbor algorithm is found to be faster compared to all SVM classifiers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".