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)

3 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 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
Pages360-370
Number of pages11
ISBN (Electronic)9783319411651
ISBN (Print)9783319411644
DOIs
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)
PublisherSpringer
Volume9757 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Blended Learning
Abbreviated titleICBL 2016
CountryChina
CityBeijing
Period19/07/1621/07/16

Fingerprint

Application programs
Websites

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).

Keywords

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

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 ; WANG, Fu Lee ; WU, Qingyuan. / 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",
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).",
year = "2016",
doi = "10.1007/978-3-319-41165-1_32",
language = "English",
isbn = "9783319411644",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer International Publishing AG",
pages = "360--370",
editor = "CHEUNG, {Simon K. S.} and Lam-for KWOK and Junjie SHANG and Aihua WANG and Reggie KWAN",
booktitle = "Blended Learning : Aligning Theory with Practices : 9th International Conference, ICBL 2016, Beijing, China, July 19-21, 2016, proceedings",
address = "Switzerland",

}

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; WANG, Fu Lee; WU, Qingyuan.

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 ProceedingsConference paper (refereed)

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

ER -

ZOU D, XIE H, WONG T-L, 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)). https://doi.org/10.1007/978-3-319-41165-1_32