The augmented hybrid graph framework for multi-level e-learning applications

Di ZOU, Haoran XIE, Tak Lam WONG, Fu Lee WANG, Qingyuan WU

Research output: Contribution to conferencePapers

3 Citations (Scopus)


The advances in MOOCs, Web learning communities, social media platforms and mobile learning apps have been witnessed in recent few years. With the development of these applications and systems, the significant growth of learning resources with multimodalities (e.g., web pages, e-books, lecture videos) has greatly changed the way people learn new knowledge and skills. However, this results in the problem of information overload as learners are overwhelmed by the rich learning resources that accompany the ever developing technologies. In other words, it is increasingly difficult for learners to find required learning materials efficiently and effectively when they confront such a large volume of data. To tackle this problem, it is essential to build a powerful framework to organize e-learning resources and capture learning preferences. In this paper, we therefore propose a graph-based framework to achieve these intended outcomes by integrating various hidden relationships among learners, users and resources. Throughout the case studies, we have verified that the proposed framework is very flexible and powerful to support various kinds of e-learning applications in different scales.
Original languageEnglish
Publication statusPublished - Jul 2016


Zou, D., Xie, H., Wong, T.-L., Wang, F. L., & Wu, Q. (2016, July). The augmented hybrid graph framework for multi-level e-learning applications. Paper presented at The International Conference on Blended Learning 2016 and International Symposium on Educational Technology 2016, Peking University, Beijing, China.


  • Graph-based model
  • E-learning systems
  • Learning preferences
  • Hidden relationship
  • Conceptual framework


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