Towards a better understanding of the role of visualization in online learning: A review

• Published in: Visual informatics (Online) Vol. 6; no. 4; pp. 22 - 33
• Main Authors: Zhang, Gefei, Zhu, Zihao, Zhu, Sujia, Liang, Ronghua, Sun, Guodao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022; Elsevier
• Subjects: Online learning; Visual analytics; Visualization in education; Visual analytics; Visualization in education; Online learning
• ISSN: 2468-502X; 2468-502X
• DOI: 10.1016/j.visinf.2022.09.002

With the popularity of online learning in recent decades, MOOCs (Massive Open Online Courses) are increasingly pervasive and widely used in many areas. Visualizing online learning is particularly important because it helps to analyze learner performance, evaluate the effectiveness of online learning platforms, and predict dropout risks. Due to the large-scale, high-dimensional, and heterogeneous characteristics of the data obtained from online learning, it is difficult to find hidden information. In this paper, we review and classify the existing literature for online learning to better understand the role of visualization in online learning. Our taxonomy is based on four categorizations of online learning tasks: behavior analysis, behavior prediction, learning pattern exploration and assisted learning. Based on our review of relevant literature over the past decade, we also identify several remaining research challenges and future research work.