Utilizing clickstream data to reveal the time management of self-regulated learning in a higher education online learning environment

ABSTRACT

Online learning with the characteristics of flexibility and autonomy has become a widespread and popular mode of higher education in which students need to engage in self-regulated learning (SRL) to achieve success. The purpose of this study is to utilize clickstream data to reveal the time management of SRL. This study adopts learning analytics to investigate the differences in time management (time investment and time use patterns) in a large-scale authentic online learning environment based on 8019 students’ clickstream data of over one term recorded by the starC system log. This study quantitatively reveals the SRL process in a higher education online learning environment, which presents the detailed differences in time management among students with different academic performance categories. These research results will have inspirations in the design of SRL interventions for optimizing students’ learning processes and overall achievement.

KEYWORDS:

• Time management
• self-regulated learning
• clickstream data
• online learning
• academic performance
• learning analytics

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant [62077020]; the Fund of China Scholarship Council under grant [202006770012]; and the Fundamental Research Funds for the Central Universities of China under grant [2020YBZZ009].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by China Scholarship Council: [Grant Number 202006770012]; National Natural Science Foundation of China: [Grant Number 62077020]; Fundamental Research Funds for the Central Universities of China: [Grant Number 2020YBZZ009].

Notes on contributors

Taihe Cao

Taihe Cao is currently a Ph.D. candidate in the National Engineering Research Center for E-Learning, Central China Normal University. He is a guest researcher in the Humboldt University of Berlin and DFKI Educational Technology Lab for one year as part of the joint-supervision PhD student program funded by China Scholarship Council. His research interests include computer-assisted instruction (CAI), self-regulated learning (SRL), and learning analytics (LA). Now, he is devoted to utilizing learning analytics to reveal learners’ SRL process and support the effective implementation of self-regulated learning.

Zhaoli Zhang

Zhaoli Zhang received the M.S. degree in computer science from Central China Normal University, Wuhan, China, in 2004, and the Ph.D. degree in computer science from Huazhong University of Science and Technology in 2008. He is currently a professor in the National Engineering Research Center for E-Learning, Central China Normal University. His research interests include self-regulated learning (SRL), signal processing, knowledge services, and software engineering. He is a member of IEEE and CCF (China Computer Federation).

Wenli Chen

Wenli Chen is an associate professor of Learning Sciences and Technology (LST) at the National Institute of Education, Nanyang Technological University Singapore. She is in charge of the PhD and EdD program of LST. She is specialized in computer-supported collaborative learning, learning analytics, and mobile learning. She has led a number of national-scale research projects and attracted funding from both Ministry of Education Singapore and the National Research Foundation of Singapore. She has published widely, received several research awards nationally and internationally. She has contributed to several educational projects with European Union (EU) and The United Nations Educational, Scientific and Cultural Organization (UNESCO).

Jiangbo Shu

Jiangbo Shu is currently an associate professor in the National Engineering Laboratory for Educational BIG DATA at the Central China Normal University. He holds a Ph.D. degree from Central China Normal University, Wuhan, China, in 2011. His research interests include computer applications, big data analysis, software engineering, and application of information technology in education.