Analyzing Graduate Students' Behaviors of Self-regulated Learning in a Blended Learning Environment

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Abstract

Previous studies have shown that learners' self-regulated learning in blended learning is closely related to their learning performance. However, it is unclear how self-regulated learners demonstrate their behaviors in blended learning environments. In this study, an academic reading and writing system was taken as a tool to collect graduate students' learning behaviors. The participants were the first-year graduate students, who took a blended course. In the course, they were required to read literature, do in/post-reading activities, take assessment and write essays in the system before classes every week. In order to further understand the behavior patterns of self-regulated graduate students, the participants were divided as high and low self-regulated learning group. The results showed that although both groups used similar times of system functions, they demonstrated different behavior patterns in terms of lag sequential analysis. Furthermore, the high self-regulated learners tended to look back at what they had done, and flexibly adjust their actions of reading and writing papers. On the contrary, instead of paper writing, the low self-regulated learners might treat tests as their goals and demonstrate simpler behavior patterns.

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Chen, W., & Cheng, H. N. H. (2020). Analyzing Graduate Students’ Behaviors of Self-regulated Learning in a Blended Learning Environment. In ACM International Conference Proceeding Series (pp. 53–57). Association for Computing Machinery. https://doi.org/10.1145/3395245.3396416

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