Educational data mining for student performance prediction: feature selection and model evaluation - Tao-Hongli (Journal of Electrical Systems, 2024)
Student performance is a multidimensional concept that includes academic accomplishment and cognitive development. It measures the performance of educational institutions, teaching methods and individual efforts. Engagement, motivation and support networks have a substantial impact on performance results. In this research, we intend to establish an intelligent data mining-based model for predicting student performance through a novel feature selection approach. We propose an innovative Adaptive Sea Horse Optimization (ASHO) to select the crucial features to predict the education performance level. We gathered a xAPI-Edu-Data dataset which includes students' numerous academic details, to train our suggested model. The Z-score normalization algorithm is employed to pre-process the obtained raw data, it improves the quality of the data. We utilized Independent Component Analysis (ICA) to extract relevant characteristics from the processed data. We utilize the ASH algorithm for feature selection, it dynamically adjusts search parameters, efficiently exploring the feature space to locate the ideal subset of features for improving the predictive performance. The selected features are classified by the implementation of the eXtreme Gradient Boosting (XGBoost) algorithm for predicting student performance. Our recommended approach is implemented in Python software. The finding evaluation phase examines the suggested model's prediction effectiveness with various parameters such as precision, accuracy, recall and fl score. We performed a comparison analysis with other traditional methods to determine the effectiveness of the proposed approach. The experimental results demonstrate that the proposed prediction approach performed better than other existing approaches.