Gefei Zhang, Zihao Zhu, Sujia Zhu, Ronghua Liang, Guodao Sun
This github repo hosts a web-based interactive browser of our survey paper on OL4VIS, which is accepted by ChinaVis 2022.
This review is published at Visual Informatics, link.
Online interactive browser: https://zjutvis.github.io/OL4VIS/
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.
@article{ZHANG202222,
title = {Towards a better understanding of the role of visualization in online learning: A review},
journal = {Visual Informatics},
volume = {6},
number = {4},
pages = {22-33},
year = {2022},
author = {Gefei Zhang and Zihao Zhu and Sujia Zhu and Ronghua Liang and Guodao Sun},
}