  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.
