MOOC Learner Profiling Using Relation-Aware Heterogeneous Graph Neural Networks

Research on learner profiling in education primarily focuses on utilizing students’ personal characteristics and behavioral data to depict their learning status and traits. However, existing methods often face challenges such as incomplete data and difficulties in feature extraction, leading to incomplete and less accurate learner profiles. To address information gaps in learner profiles within educational datasets, this study proposes a profile completion technique based on relation-aware heterogeneous graph networks. Using the MOOCCube and MOOCCubeX datasets, we trained a relation-aware heterogeneous graph network model to predict students’ age and gender. The model achieved significant advancements in gender prediction. While age prediction performance remains relatively low—a common challenge in the field due to the subtle and multifaceted nature of age-related behavioral signals—ablation studies confirm the model’s robustness, demonstrating stable gender prediction accuracy even with significant data reduction. This work bridges information gaps in learner profiling within educational datasets, providing crucial support for personalized education and teaching quality improvement. It showcases the potential application of relation-aware heterogeneous graph networks in education and offers new ideas for research utilizing heterogeneous graph networks for learner profiling.