Exploring Recommending Algorithms Applied in e-Learning Environments

Ngai Cheong

doi:10.1145/3568834.3568869

Exploring Recommending Algorithms Applied in e-Learning Environments

Ngai Cheong

Faculty of Applied Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recommending Algorithm (RA) applied in e-learning may provide the students with a personalized e-Learning environments, by automatically identifying their preferences and the situations. At present, the widely used RAs have some limitations in e-learning environments and there is a certain gap with the set goals. This paper explores these application limitations of RAs that may be used in e-learning environments, which may not fulfill the specific requirements and the students' cognition. And the suggestions for solving these problems are also discussed in the paper, in order to improve the effectiveness and accuracy of recommending operations.

Original language	English
Title of host publication	2022 8th International Conference on Industrial and Business Engineering, ICIBE 2022
Publisher	Association for Computing Machinery
Pages	526-530
Number of pages	5
ISBN (Electronic)	9781450397582
DOIs	https://doi.org/10.1145/3568834.3568869
Publication status	Published - 27 Sept 2022
Event	8th International Conference on Industrial and Business Engineering, ICIBE 2022 - Virtual, Online, China Duration: 27 Sept 2022 → 29 Sept 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	8th International Conference on Industrial and Business Engineering, ICIBE 2022
Country/Territory	China
City	Virtual, Online
Period	27/09/22 → 29/09/22

Keywords

Cognition
Effectiveness
Hybrid Method in e-Learning Environments
Limitation
Recommending Algorithm

Access to Document

10.1145/3568834.3568869

Cite this

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title = "Exploring Recommending Algorithms Applied in e-Learning Environments",

abstract = "Recommending Algorithm (RA) applied in e-learning may provide the students with a personalized e-Learning environments, by automatically identifying their preferences and the situations. At present, the widely used RAs have some limitations in e-learning environments and there is a certain gap with the set goals. This paper explores these application limitations of RAs that may be used in e-learning environments, which may not fulfill the specific requirements and the students' cognition. And the suggestions for solving these problems are also discussed in the paper, in order to improve the effectiveness and accuracy of recommending operations.",

keywords = "Cognition, Effectiveness, Hybrid Method in e-Learning Environments, Limitation, Recommending Algorithm",

author = "Ngai Cheong",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 8th International Conference on Industrial and Business Engineering, ICIBE 2022 ; Conference date: 27-09-2022 Through 29-09-2022",

year = "2022",

month = sep,

day = "27",

doi = "10.1145/3568834.3568869",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "526--530",

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}

Cheong, N 2022, Exploring Recommending Algorithms Applied in e-Learning Environments. in 2022 8th International Conference on Industrial and Business Engineering, ICIBE 2022. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 526-530, 8th International Conference on Industrial and Business Engineering, ICIBE 2022, Virtual, Online, China, 27/09/22. https://doi.org/10.1145/3568834.3568869

Exploring Recommending Algorithms Applied in e-Learning Environments. / Cheong, Ngai.
2022 8th International Conference on Industrial and Business Engineering, ICIBE 2022. Association for Computing Machinery, 2022. p. 526-530 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Recommending Algorithm (RA) applied in e-learning may provide the students with a personalized e-Learning environments, by automatically identifying their preferences and the situations. At present, the widely used RAs have some limitations in e-learning environments and there is a certain gap with the set goals. This paper explores these application limitations of RAs that may be used in e-learning environments, which may not fulfill the specific requirements and the students' cognition. And the suggestions for solving these problems are also discussed in the paper, in order to improve the effectiveness and accuracy of recommending operations.

KW - Cognition

KW - Effectiveness

KW - Hybrid Method in e-Learning Environments

KW - Limitation

KW - Recommending Algorithm

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Exploring Recommending Algorithms Applied in e-Learning Environments

Abstract

Publication series

Conference

Keywords

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