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|Title:||Self and Socially Shared Regulation of Learning in Data Science Education: A Case Study of “Quantified Self” Project|
|Keywords:||Learning and Identity|
|Publisher:||International Society of the Learning Sciences (ISLS)|
|Citation:||Zhang, J. & Wu, B. (2020). Self and Socially Shared Regulation of Learning in Data Science Education: A Case Study of “Quantified Self” Project. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 749-750). Nashville, Tennessee: International Society of the Learning Sciences.|
|Abstract:||This study explored the influence of student self-regulation (SRL) ability on socially shared regulation of learning (SSRL) and data literacy presented in a data-driven research project. We adopted a process-oriented method for analyzing video recordings of group conversations in the project meetings. Results showed that the high SRL group was tended to engage more in SSRL and critical components of data literacy than the low SRL group. Implications of the study are also discussed.|
|Appears in Collections:||ICLS 2020|
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