A survey of visual analytics techniques for online education

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2022-12-01 DOI:10.1016/j.visinf.2022.07.004

Xiaoyan Kui, Naiming Liu, Qiang Liu, Jingwei Liu, Xiaoqian Zeng, Chao Zhang

{"title":"A survey of visual analytics techniques for online education","authors":"Xiaoyan Kui, Naiming Liu, Qiang Liu, Jingwei Liu, Xiaoqian Zeng, Chao Zhang","doi":"10.1016/j.visinf.2022.07.004","DOIUrl":null,"url":null,"abstract":"<div><p>Visual analytics techniques are widely utilized to facilitate the exploration of online educational data. To help researchers better understand the necessity and the efficiency of these techniques in online education, we systematically review related works of the past decade to provide a comprehensive view of the use of visualization in online education problems. We establish a taxonomy based on the analysis goal and classify the existing visual analytics techniques into four categories: learning behavior analysis, learning content analysis, analysis of interactions among students, and prediction and recommendation. The use of visual analytics techniques is summarized in each category to show their benefits in different analysis tasks. At last, we discuss the future research opportunities and challenges in the utilization of visual analytics techniques for online education.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 67-77"},"PeriodicalIF":3.8000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000870/pdfft?md5=9da41107a6cadbfebb837a6957330648&pid=1-s2.0-S2468502X22000870-main.pdf","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Informatics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468502X22000870","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 3

Abstract

Visual analytics techniques are widely utilized to facilitate the exploration of online educational data. To help researchers better understand the necessity and the efficiency of these techniques in online education, we systematically review related works of the past decade to provide a comprehensive view of the use of visualization in online education problems. We establish a taxonomy based on the analysis goal and classify the existing visual analytics techniques into four categories: learning behavior analysis, learning content analysis, analysis of interactions among students, and prediction and recommendation. The use of visual analytics techniques is summarized in each category to show their benefits in different analysis tasks. At last, we discuss the future research opportunities and challenges in the utilization of visual analytics techniques for online education.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

在线教育的可视化分析技术调查

可视化分析技术被广泛用于促进在线教育数据的探索。为了帮助研究人员更好地理解这些技术在在线教育中的必要性和效率，我们系统地回顾了过去十年的相关工作，以提供可视化在在线教育问题中的应用的全面观点。我们基于分析目标建立了分类法，将现有的可视化分析技术分为四类:学习行为分析、学习内容分析、学生互动分析、预测与推荐。在每个类别中总结了可视化分析技术的使用，以显示它们在不同分析任务中的好处。最后，我们讨论了可视化分析技术在在线教育中应用的未来研究机遇和挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊