Class-Imbalanced Dynamic Feature Selection for Dropout Prediction in Virtual Learning Environments

Prediction of student dropout is a major challenge for educational institutions. Although AI-based approaches can predict dropout through data analysis, they often rely on a static set of features and fail to adapt to the evolving nature of data over time, thus limiting performance. Other application domains have the same characteristic of evolving data and several algorithms have been proposed to ensure dynamic feature selection over time. However, they are not adapted to dropout prediction, characterized by imbalanced data, which limits their effectiveness. In this paper we introduce CI-DFS, a novel algorithm that achieves dynamic feature selection on class imbalanced data and used to perform dropout prediction. CI-DFS relies on three main elements: 1) a weighted mutual information-based metric to deal with imbalanced class distributions, 2) an adaptive threshold mechanism to dynamically assess feature relevance, and 3) a temporal drift management system to ensure feature relevance over time. CI-DFS has been evaluated on two real-world benchmark educational datasets, and compared with a state-of-the-art dynamic feature selection algorithm. CI-DFS outperforms this algorithm in three key aspects: it improves dropout prediction by 9%, reduces the number of features selected by over 50%, and significantly accelerates feature selection time, making it 10 times faster.