Improved Explainable Educational Data Mining System for Enhancing Programming Skills

Abstract

Forecasting student academic performance benefits from the extremely effective method known as educational data mining (EDM), which also helps to find important links within educational data. Evaluating and improving students' programming competency has been the main emphasis of many recent studies. Still, there are chances for constant development in this field. In this work, we provide an improved and understandable Educational Data Mining (EDM) approach for spotting and improving students' programming capacity. This proposed EDM system seeks to investigate a very effective feature engineering approach, a suitable classification technique, and the use of Explainable Artificial Intelligence (XAI) tools for model explanation. We do ablation study to find the best feature engineering method. The categorizing process decides students' current programming state. Six basic Machine Learning (ML) algorithms—decision tree, Support Vector Machine, Random Forest (RF), artificial neural network, Naive Bayes Classifier, k-Nearest Neighbor, and Ensemble method—are the main subjects of this module. Many criteria—including accuracy, precision, recall, f1-score, ROC curve, McNamar test, and others—are used to assess the performance of these algorithms. The experimental results show that among the many models, the Random Forest (RF) and the Stacking-SRDA ensemble technique can classify the students with more accuracy than others. To improve the interpretability of the model, we have finally used XAI technologies like Eli5, SHAPASH, and Local Interpretable Model Agnostic Explanations