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An Integrated Interpretable Performance Prediction Model with Dynamically Optimized Attributes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 88 |
| Year of Publication: 2026 |
| Authors: Meenakshi Devi, Rakesh Kumar |
10.5120/ijca2026926521
|
Meenakshi Devi, Rakesh Kumar . An Integrated Interpretable Performance Prediction Model with Dynamically Optimized Attributes. International Journal of Computer Applications. 187, 88 ( Mar 2026), 1-8. DOI=10.5120/ijca2026926521
Abstract
The increasing availability of educational data has opened new possibilities for applying machine learning. It helps improve student learning outcomes. This paper examines the application of an integrated machine learning approach to an educational dataset. It keeps the focus on key modeling attributes that influence student performance. The emphasis is on selecting only meaningful attributes to reduce irrelevant information. To ensure stable, reliable modeling, the model validation is incorporated in the integrated approach. The model evaluation using cross-validation establishes confidence in the results' reliability. An optimization technique follows cross-validation. It searches the relevant attributes, thereby simplifying the model and making it easier to interpret. In addition to evaluating predictive performance metrics, model interpretation is included for the selected attributes. The interpretation analysis enables a better understanding of the relevant attributes across different models. SHAP is used to illustrate the contribution of individual attributes to prediction outcomes. The findings report that combining validation, optimization, and interpretability enhances the model performance for the educational data.
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