TY - GEN
T1 - The augmented hybrid graph framework for multi-level e-learning applications
AU - ZOU, Di
AU - XIE, Haoran
AU - WONG, Tak-Lam
AU - WANG, Fu Lee
AU - WU, Qingyuan
N1 - 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).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Graph-based model
KW - E-learning systems
KW - Learning preferences
KW - Hidden relationship
KW - Conceptual framework
UR - http://www.scopus.com/inward/record.url?scp=84978946897&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-41165-1_32
DO - 10.1007/978-3-319-41165-1_32
M3 - Conference paper (refereed)
SN - 9783319411644
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 370
BT - Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings
A2 - CHEUNG, Simon K. S.
A2 - KWOK, Lam-for
A2 - SHANG, Junjie
A2 - WANG, Aihua
A2 - KWAN, Reggie
PB - Springer International Publishing AG
T2 - 9th International Conference on Blended Learning
Y2 - 19 July 2016 through 21 July 2016
ER -