TY - GEN
T1 - A Review on Visualization of Educational Data in Online Learning
AU - Dewan, M. Ali Akber
AU - Pachon, Walter Moreno
AU - Lin, Fuhua
N1 - Funding Information:
Acknowledgement. This research was supported by NSERC Discovery Grant, and Academic and Professional Development Grant, Athabasca University, Canada.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Higher educational institutions capture huge amounts of educational data, especially in online learning. Data mining techniques have shown promises to interpret these data using different patterns. However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. Visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. This review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.
AB - Higher educational institutions capture huge amounts of educational data, especially in online learning. Data mining techniques have shown promises to interpret these data using different patterns. However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. Visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. This review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.
KW - Educational data mining
KW - Information visualization
KW - Learning management system
KW - Pattern recognition
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85101511202&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-66906-5_2
DO - 10.1007/978-3-030-66906-5_2
M3 - Published Conference contribution
AN - SCOPUS:85101511202
SN - 9783030669058
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 24
BT - Learning Technologies and Systems - 19th International Conference on Web-Based Learning, ICWL 2020, and 5th International Symposium on Emerging Technologies for Education, SETE 2020, Proceedings
A2 - Pang, Chaoyi
A2 - Gao, Yunjun
A2 - Chen, Guanliang
A2 - Popescu, Elvira
A2 - Chen, Lu
A2 - Hao, Tianyong
A2 - Zhang, Bailing
A2 - Navarro, Silvia Margarita
A2 - Li, Qing
T2 - 19th International Conference on Web-Based Learning, ICWL 2020 and 5th International Symposium on Emerging Technologies for Education, SETE 2020
Y2 - 22 October 2020 through 24 October 2020
ER -