Using ML and Graph Convolution Network Models to Identify At-Risk Students by Extracting Topological Features

The study "Extracting topological features to identify at-risk students using machine learning and graph convolutional network models" aims to develop a dependable and precise machine learning approach for predicting students' academic performance and identifying those at risk of subpar academic outcomes. The study proposes a method that incorporates graph theory and topological features to obtain a more profound understanding and reveal structural correlations within the data.

The methodology used in the study involved converting tabular data into graphs using distance measures such as Euclidean and Cosine to assess similarities between students' data and construct a graph. The researchers then extracted graph topological features (GF) from the graph to enrich their data and reveal structural correlations. These topological features were merged with the original tabular dataset, and various feature selection methods were utilized to identify the most impactful features in the enhanced dataset.

The students were categorized into three classes based on the final dataset attributes - good, at-risk, or failed. To compare the performance of conventional machine learning-based models, the study implemented a graph-based convolutional network (GCN). Furthermore, the researchers developed a knowledge graph using the features of the proposed dataset to examine the relationships among different features. The proposed method was designed to identify at-risk students using knowledge graphs and conventional machine learning methods.

The team was led by Professor Nazar Zaki, a specialist in computer science and machine learning, and included Dr. Balqis Albreiki, an expert in machine learning, data mining, and graph theory, and Dr. Tetiana Habuza, an expert in machine learning, medical image, and clinical data analysis.

The findings of this study have significant implications for improving student success rates and reducing low performance rates in higher education. By accurately identifying at-risk students early on, interventions and support systems can be put in place to improve their chances of success. Additionally, the use of graph theory and topological features provide valuable insights into the factors that contribute to academic performance, enabling targeted interventions to address specific challenges faced by students. The multidisciplinary collaboration among experts in machine learning, graph theory, and education allowed for a comprehensive approach to the research, making important contributions to the field of educational technology.