What is it about?
How do Chinese people who are doing graduate studies in Japan recognize self-regulated learning? Do they engage in self-regulated learning? Is this type of learning beneficial to their graduate studies? Does the large scale online class format affect their self-regulated learning? I explored how these students learn and are impacted in these challenging times., and the context is representative of the current COVID-19 pandemic period.
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Why is it important?
This is unique because most studies select local students from one country, but this study selected a special group of Chinese graduate students to study in Japan. It is more challenging in terms of cross-cultural adaptation and learning ability, and they face more challenges and unknowns than other graduate students. Second, it is also influential because current research on self-regulated learning is relatively rich, yet focusing on participants at the compulsory education level and undergraduate students, and little research has been conducted on graduate level students. In addition, this group is already capable of some degree of self-regulated learning and is therefore more representative and persuasive in providing more contextual and supportive evidence for addressing learning challenges and global change.
Perspectives
This is an important study because despite the increasing research on student SRL learning during the COVID-19 pandemic. However, little research has been conducted on SRL and international student learning, with most studies using quantitative methods but lacking deeper exploration of the perceptions and cognitions of these students. In this study, I chose a qualitative approach to conduct in-depth interviews and to explore the specific learning situationof Chinese international students in Japan.
MIAOMIAO JIA
Bristol University
Read the Original
This page is a summary of: The influence of distance learning during COVID-19 pandemic on student's self-regulated learning in higher education: A qualitative study, September 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3488466.3488492.
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