A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs
This addresses the problem of identifying at-risk students in online education, but it is incremental as it applies an existing method to new data with a comparative analysis.
The study tackled predicting student performance in online courses by analyzing a heterogeneous knowledge graph with a Graph Convolutional Network, achieving 70-90% accuracy in predicting whether students would pass problem sets based on content consumption and course modality.
As online courses become the norm in the higher-education landscape, investigations into student performance between students who take online vs on-campus versions of classes become necessary. While attention has been given to looking at differences in learning outcomes through comparisons of students' end performance, less attention has been given in comparing students' engagement patterns between different modalities. In this study, we analyze a heterogeneous knowledge graph consisting of students, course videos, formative assessments and their interactions to predict student performance via a Graph Convolutional Network (GCN). Using students' performance on the assessments, we attempt to determine a useful model for identifying at-risk students. We then compare the models generated between 5 on-campus and 2 fully-online MOOC-style instances of the same course. The model developed achieved a 70-90\% accuracy of predicting whether a student would pass a particular problem set based on content consumed, course instance, and modality.