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

• Published in: Interactive learning environments 2023-12, Vol.31 (10), p.6555-6572
• Main Authors: Cao, Taihe, Zhang, Zhaoli, Chen, Wenli, Shu, Jiangbo
• Format: Article
• Language: English
• Subjects: academic performance; clickstream data; Design optimization; Distance learning; Higher education; Learning analytics; Online instruction; online learning; Self regulation; self-regulated learning; Students; Time management
• ISSN: 1049-4820; 1744-5191
• DOI: 10.1080/10494820.2022.2042031

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.