Heterogeneous Information Enhanced Prerequisite Learning in Massive Open Online Courses

The knowledge concept prerequisites describing the dependencies are critical for fundamental tasks such as material recommendations and there are a huge amount of concepts in Massive Open Online Courses (MOOCs). Thus it is necessary to develop automatic prerequisite relation annotation methods. Recently, a few methods have shown their effectiveness in discovering knowledge concept prerequisites in Moocs automatically. However, they suffer from two common issues, i.e., knowledge concepts are not thoroughly learnt, and informative supervision sources are ignored. To overcome these issues, we propose an end-to-end framework to incorporate the rich heterogeneous information in MOOCs, including the semantic, contextual and structural information of the learning materials as well as student video watching behaviors. Such useful information is not only used to derive entity representations but also as supervision to improve the prerequisite learning task. Experimental results on two public datasets show that the proposed framework outperforms state-of-the-art baselines in terms of precision, recall and F1 values and improves up to 9% in terms of F1 metrics. Besides, ablation study demonstrates the effectiveness of the proposed framework.