# Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

arXiv: https://arxiv.org/abs/2310.12281 · HTML: https://mlsanity.com/paper/2310.12281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources](https://mlsanity.com/paper/2011.14867/) (2020)
- [Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network](https://mlsanity.com/paper/2008.01613/) (2020)
- [Predicting Performance on MOOC Assessments using Multi-Regression Models](https://mlsanity.com/paper/1605.02269/) (2016)
- [Domain Adaptation for Real-Time Student Performance Prediction](https://mlsanity.com/paper/1809.06686/) (2018)

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

- [Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction](https://mlsanity.com/paper/2409.14192/) (2024)

## Abstract (arXiv)

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in urlhttps://github.com/DSAatUSU/MOOPergradeprediction