Data Mining in MOOCs
Recent years, MOOCs have become increasingly popular and offered students around the world the opportunity to take online courses from different universities. The large and growing amounts of online education data present both open challenges and significant opportunities for data mining research to enrich educational offerings. From the year 2016 to 2017, I have been working on how to efficiently discover the knowledge concepts in MOOCs and also discover the relationships between these concepts.
Cross-lingual Knowledge Graph
The global knowledge sharing makes large-scale multi-lingual knowledge bases an extremely valuable resource in the Big Data era. In the year 2015, my research mainly focus on cross-lingual knowledge graph construction, and I also research on cross-lingual knowledge linking, which aims to discover and link equivalent entities between multi-lingual knowledge bases.
Selected Research Work
Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation (IJCNLP'17)
Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li and Jie Tang
|Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs is how to design effective and fine-grained course concepts such that students with different backgrounds can grasp the essence of the course. In this paper, we conduct a systematic investigation of the problem of course concept extraction for MOOCs. We propose to learn latent representations for candidate concepts via an embedding-based method. Moreover, we develop a graph-based propagation algorithm to rank the candidate concepts based on the learned representations. We evaluate the proposed method using different courses from XuetangX and Coursera. Experimental results show that our method significantly outperforms all the alternative methods.|
Prerequisite Relation Learning for Concepts in MOOCs (ACL'17)
Liangming Pan, Chengjiang Li, Juanzi Li and Jie Tang
|What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leveraged to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0% by F1-score) comparing with existing methods.|