Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis

Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis

This paper explores the personalized approach of the public opinion cluster analysis for learning resources based on the server-side predetermined analysis, in order to introduce the personalized learning resource recommender into the traditional online instruction. In allusion to further validation...

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Bibliographic Details
Main Authors: Haiyun Li, Xuebo Zhang, Junhui Wang
Format: Article
Language:English
Published: Kassel University Press 2017-12-01
Series:International Journal of Emerging Technologies in Learning (iJET)
Subjects:
personalized learning recommender
public opinion
text clustering
Online Access:http://online-journals.org/index.php/i-jet/article/view/7965
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spelling doaj-c116d1472f264bf29476f772889ef1672020-11-24T21:44:51ZengKassel University PressInternational Journal of Emerging Technologies in Learning (iJET)1863-03832017-12-01121211211810.3991/ijet.v12i12.79653505Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster AnalysisHaiyun Li0Xuebo Zhang1Junhui Wang2South China Normal UniversitySouth China Normal UniversitySouth China Normal UniversityThis paper explores the personalized approach of the public opinion cluster analysis for learning resources based on the server-side predetermined analysis, in order to introduce the personalized learning resource recommender into the traditional online instruction. In allusion to further validation on its implementation, the fuzzy aggregation of learning resources is mined up based on the proposed WRTC algorithm. The personalized learning resource recommender mechanism is then described. In the end, the common evaluation parameters in the personalized recommender model are applied in the evaluation on the system performance. The experiment is carried out with learner's access data online to validate whether the algorithm and the model indicators are effective for the purpose of improving the precision and coverage of learning resources.http://online-journals.org/index.php/i-jet/article/view/7965personalized learning recommenderpublic opiniontext clustering
collection DOAJ
language English
format Article
sources DOAJ
author Haiyun Li
Xuebo Zhang
Junhui Wang
spellingShingle Haiyun Li
Xuebo Zhang
Junhui Wang
Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
International Journal of Emerging Technologies in Learning (iJET)
personalized learning recommender
public opinion
text clustering
author_facet Haiyun Li
Xuebo Zhang
Junhui Wang
author_sort Haiyun Li
title Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
title_short Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
title_full Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
title_fullStr Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
title_full_unstemmed Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
title_sort discovery and recommendation of first-hand learning resources based on public opinion cluster analysis
publisher Kassel University Press
series International Journal of Emerging Technologies in Learning (iJET)
issn 1863-0383
publishDate 2017-12-01
description This paper explores the personalized approach of the public opinion cluster analysis for learning resources based on the server-side predetermined analysis, in order to introduce the personalized learning resource recommender into the traditional online instruction. In allusion to further validation on its implementation, the fuzzy aggregation of learning resources is mined up based on the proposed WRTC algorithm. The personalized learning resource recommender mechanism is then described. In the end, the common evaluation parameters in the personalized recommender model are applied in the evaluation on the system performance. The experiment is carried out with learner's access data online to validate whether the algorithm and the model indicators are effective for the purpose of improving the precision and coverage of learning resources.
topic personalized learning recommender
public opinion
text clustering
url http://online-journals.org/index.php/i-jet/article/view/7965
work_keys_str_mv AT haiyunli discoveryandrecommendationoffirsthandlearningresourcesbasedonpublicopinionclusteranalysis
AT xuebozhang discoveryandrecommendationoffirsthandlearningresourcesbasedonpublicopinionclusteranalysis
AT junhuiwang discoveryandrecommendationoffirsthandlearningresourcesbasedonpublicopinionclusteranalysis
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