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: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

4 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
Title of host publicationBlended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings
EditorsSimon K. S. CHEUNG, Lam-for KWOK, Junjie SHANG, Aihua WANG, Reggie KWAN
PublisherSpringer International Publishing AG
Number of pages11
ISBN (Electronic)9783319411651
ISBN (Print)9783319411644
Publication statusPublished - 2016
Externally publishedYes
Event9th International Conference on Blended Learning - Peking University, Beijing, China
Duration: 19 Jul 201621 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9757 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Blended Learning
Abbreviated titleICBL 2016

Bibliographical note

The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), the Internal Research Grant (RG 30/2014-2015) of the Hong Kong Institute of Education and a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013).


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


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