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

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

doi:10.1007/978-3-319-41165-1_32

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 Proceedings › Conference paper (refereed) › Research

4 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings
Editors	Simon K. S. CHEUNG, Lam-for KWOK, Junjie SHANG, Aihua WANG, Reggie KWAN
Publisher	Springer International Publishing AG
Pages	360-370
Number of pages	11
ISBN (Electronic)	9783319411651
ISBN (Print)	9783319411644
DOIs	https://doi.org/10.1007/978-3-319-41165-1_32
Publication status	Published - 2016
Externally published	Yes
Event	9th International Conference on Blended Learning - Peking University, Beijing, China Duration: 19 Jul 2016 → 21 Jul 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	9757 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Blended Learning
Abbreviated title	ICBL 2016
Country/Territory	China
City	Beijing
Period	19/07/16 → 21/07/16

Funding

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).

Keywords

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

Access to Document

10.1007/978-3-319-41165-1_32

Cite this

ZOU, D., XIE, H., WONG, T.-L., WANG, F. L., & WU, Q. (2016). The augmented hybrid graph framework for multi-level e-learning applications. In S. K. S. CHEUNG, L. KWOK, J. SHANG, A. WANG, & R. KWAN (Eds.), Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings (pp. 360-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9757 LNCS). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-41165-1_32

ZOU, Di ; XIE, Haoran ; WONG, Tak-Lam et al. / The augmented hybrid graph framework for multi-level e-learning applications. Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings. editor / Simon K. S. CHEUNG ; Lam-for KWOK ; Junjie SHANG ; Aihua WANG ; Reggie KWAN. Springer International Publishing AG, 2016. pp. 360-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e0f1f13d9d0a4798beecbed2285369e1,

title = "The augmented hybrid graph framework for multi-level e-learning applications",

abstract = "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.",

keywords = "Graph-based model, E-learning systems, Learning preferences, Hidden relationship, Conceptual framework",

author = "Di ZOU and Haoran XIE and Tak-Lam WONG and WANG, {Fu Lee} and Qingyuan WU",

year = "2016",

doi = "10.1007/978-3-319-41165-1_32",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings",

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note = "9th International Conference on Blended Learning, ICBL 2016 ; Conference date: 19-07-2016 Through 21-07-2016",

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ZOU, D , XIE, H, WONG, T-L, WANG, FL & WU, Q 2016, The augmented hybrid graph framework for multi-level e-learning applications. in SKS CHEUNG, L KWOK, J SHANG, A WANG & R KWAN (eds), Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9757 LNCS, Springer International Publishing AG, pp. 360-370, 9th International Conference on Blended Learning, Beijing, China, 19/07/16. https://doi.org/10.1007/978-3-319-41165-1_32

The augmented hybrid graph framework for multi-level e-learning applications. / ZOU, Di ; XIE, Haoran; WONG, Tak-Lam et al.
Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings. ed. / Simon K. S. CHEUNG; Lam-for KWOK; Junjie SHANG; Aihua WANG; Reggie KWAN. Springer International Publishing AG, 2016. p. 360-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9757 LNCS).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research

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

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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

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SN - 9783319411644

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - CHEUNG, Simon K. S.

A2 - KWOK, Lam-for

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ZOU D , XIE H, WONG TL, WANG FL, WU Q. The augmented hybrid graph framework for multi-level e-learning applications. In CHEUNG SKS, KWOK L, SHANG J, WANG A, KWAN R, editors, Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings. Springer International Publishing AG. 2016. p. 360-370. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2016 Jun 17. doi: 10.1007/978-3-319-41165-1_32

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

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