Extracting topological features to identify at-risk students using machine learning and graph convolutional network models

Abstract

Technological advances have significantly affected education, leading to the creation of online learning platforms such as virtual learning environments and massive open online courses. While these platforms offer a variety of features, none of them incorporates a module that accurately predicts students’ academic performance and commitment. Consequently, it is crucial to design machine learning (ML) methods that predict student performance and identify at-risk students as early as possible. Graph representations of student data provide new insights into this area. This paper describes a simple but highly accurate technique for converting tabulated data into graphs. We employ distance measures (Euclidean and cosine) to calculate the similarities between students’ data and construct a graph. We extract graph topological features (GF) to enhance our data. This allows us to capture structural correlations among the data and gain deeper insights than isolated data analysis. The initial dataset (DS) and GF can be used alone or jointly to improve the predictive power of the ML method. The proposed method is tested on an educational dataset and returns superior results. The use of DS alone is compared with the use of $$DS + GF$$ D S + G F in the classification of students into three classes: “failed”,“at risk”, and “good”. The area under the receiver operating characteristic curve (AUC) reaches 0.948 using DS, compared with 0.964 for $$DS + GF$$ D S + G F . The accuracy in the case of $$DS + GF$$ D S + G F varies from 84.5 to 87.3%. Adding GF improves the performance by 2.019% in terms of AUC and 3.261% in terms of accuracy. Moreover, by incorporating graph topological features through a graph convolutional network (GCN), the prediction performance can be enhanced by 0.5% in terms of accuracy and 0.9% in terms of AUC under the cosine distance matrix. With the Euclidean distance matrix, adding the GCN improves the prediction accuracy by 3.7% and the AUC by 2.4%. By adding graph embedding features to ML models, at-risk students can be identified with 87.4% accuracy and 0.97 AUC. The proposed solution provides a tool for the early detection of at-risk students. This will benefit universities and enhance their prediction performance, improving both effectiveness and reputation.

Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI

“…However, it has not yet been applied to the context of employee turnover. KG-based solutions have exhibited remarkable results in diverse fields, such as healthcare [52] and education [51]. In this study, we have implemented KG approach on HR datasets containing employee information to uncover latent connections among employees.…”

“…The initial goal of our framework involves investigating potential techniques for transforming tabulated data into a knowledge graph, mirroring approaches already utilized within the domains of education [51] and healthcare [52].…”