Recommender System Utilizing Learning Style: Systematic Literature Review
Recommender System Utilizing Learning Style: Systematic Literature Review
复制标题
利用学习方式的推荐系统：系统文献综述
DOI：
10.1109/icbir52339.2021.9465832
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Suto Hidetsugu
中科院分区：
文献类型：
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作者：
Thongchotchat Vivat;Sato Kazuhiko;Suto Hidetsugu
学习风格是学习的偏好方式，可以与推荐系统结合开发计算机支持学习系统，为每个特定的学习者制定量身定制的学习路径。本研究通过系统的文献综述，对近期发表的利用可信来源的推荐系统的相关文章进行梳理；IEEE explore和ScienceDirect；然后使用设计的搜索关键词和标准提取信息，以回答最近开发的利用学习风格的推荐系统中最常用的学习风格理论和推荐算法。综述研究发现，Felder & Silverman的理论是使用最多的理论，占所有综述文章的72.5%，而适宜应用是使用最多的推荐算法，占所有综述文章的42.5%。
Learning style is the preference way of learning which can be applied with recommender system to develop the computer-support learning system which can make the tailored learning path for each particular learner. This study did the systematic literature review to gain insight of gathered recently published articles involving recommender system utilizing recommender system from trustable sources; IEEE Xplore and ScienceDirect; using designed search keywords and criteria then extracted information for answering what is the most used learning style theory and recommender algorithm in recently developed recommender systems utilizing learning style. The review study found that Felder & Silverman's theory has been the most used theory with 72.5% of all reviewed articles and suitable application is the most used recommender algorithm with 42.5% of all.