# A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs

arXiv: https://arxiv.org/abs/2407.12153 · HTML: https://mlsanity.com/paper/2407.12153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 2 of 2)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Student Success Prediction in MOOCs](https://mlsanity.com/paper/1711.06349/) (2017)

## Built on by (most cited first, 1 of 1)

- [Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features](https://mlsanity.com/paper/2505.09287/) (2025)

## Abstract (arXiv)

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.