Enhancing Predictive Performance in Identifying At-Risk Students: Integration of Topological Features, Node Embeddings in Machine Learning Models

This study proposes an innovative approach to the early identification of at-risk students in higher education settings by augmenting traditional machine learning classifiers with topological features and node embeddings derived from graph-based representations of student data from programming courses at the United Arab Emirates University. Utilizing a dataset comprising student demographic characteristics and course performance results, we construct an adjacency matrix using cosine and Canberra distance metrics, which is then thresholded to form a binary, unweighted graph. Topological features and node embeddings are extracted from this graph, representing the relationships and interactions between students. The extracted features are combined with the original dataset to train various machine learning classifiers, aiming to enhance their predictive accuracy. Experimental results demonstrate a significant improvement in prediction performance, with an increase of almost 10% in accuracy when node embeddings are incorporated. The most notable improvement was observed when using a Multi-Layer Perceptron classifier with the original dataset supplemented with both topological features and node embeddings, achieving 93.5% accuracy. Our findings highlight the potential of graph-based methods to enrich the feature set used by machine learning models, thereby enhancing their capacity to identify at-risk students early. Future work will focus on refining the feature extraction process, exploring other graph methods, and incorporating additional types of data. This study lays the foundation for more comprehensive and effective early-warning systems in higher education, aiming to enhance student support services and improve overall educational outcomes.