Optimization of Hyperparameters in Machine Learning for Enhancing Predictions of Student Academic Performance
Bibliographic record
Abstract
The prediction of student academic performance with high accuracy is of paramount importance in improving educational outcomes and developing tailored learning methodologies.It also serves as a preventative measure against student dropouts.This study is centered on enhancing the precision of such predictions by optimizing hyperparameters in machine learning techniques.In pursuit of optimal performance, a range of machine learning techniques is compared, and the most accurate one selected for hyperparameter optimization.The adopted method for this optimization is the Grid Search (GS) technique.It is found that hyperparameter optimization in the Gradient Boosting Regression Tree (GBRT) using the GS method bolsters the accuracy of predictions pertaining to student academic performance.The results obtained in this study are validated using a five-fold cross-validation method.This rigorous validation ensures the robustness of our findings.Thus, the study presents a critical contribution to the effective prediction of student academic performance, potentially informing the development of more efficient and personalized educational strategies.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".