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This study establishes a data analytics-based self-regulated learning scaffolding model for primary students and evaluates it in teaching practice. 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Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, Hubei, China.  \n2. School of Information Technology in Education, South China Normal University, Guangzhou 510631, Guangdong, China.  \n3. Weifang Institute of Education Information, Weifang 261045, Shandong, China  \n\n| Abstract: In the age of Internet Plus, the deep integration of information technology into education and individualized instruction have become a growing trend in education development. Self-regulated learning is a key element of student core competence, but easy to be overlooked in basic education. The purpose of this study is to establish the data analyticsbased self-regulated learning scaffolding model for primary students and test its results in teaching practice, citing Scaffolding Theory and Zone of Proximal Development Theory as its rationale. Research findings demonstrate that learning data analytics-enabled self-regulated afterclass learning can help enhance learning outcomes and develop student learning strategies.\u003Cbr>Best Evidence in Chinese Education 2022; 11(2):1529-1533.\u003Cbr>Doi: 10.15354/bece.22.ab006 |\n| --- |\n| How to Cite: Wang, H., Huang, T., Tian, J., Yang, H., & Han, P. (2022). An empirical study on the data analytics-based self-regulated learning scaffolding model for primary students. Best Evidence in Chinese Education, 11(2):1529-1533. |\n\nKeywords: Learning Analytics, Scaffolding, Self-regulated Learning, Learning Effectiveness  \nCorrespondence to: Tao Huang, Faculty of Artificial Intelligence in Education, Central China Normal University,  \nWuhan 430079, Hubei, China. E-mail: [tmht@mail.ccnu.edu.cn](tmht@mail.ccnu.edu.cn)  \n[Conflict of Interests](Conflict of Interests: None)[: None](Conflict of Interests: None).  \n© 2022 Insights Publisher. All rights reserved.  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License  \n([http://www.creativecommons.org/licenses/by-nc/4.0/](http://www.creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed by the Insights Publisher.  \nIntroduction  \nThe integration of big data and artificial intelligence into education and teaching is a driving force for more scientific and goal-oriented development of learning, teaching, and administration. Huge amounts of data have been amassed through expanded application of online learning platforms. Learning data mining, analytics, and modeling allow teachers to precisely grasp student learning situation and to improve instructional efficiency and effectiveness; education administrators can equalize the distribution of educational resources. It has been acknowledged in relevant investigations that selfregulated learning ability is a key component of student core competence and a foundation for further education; and that educational technology can serve as scaffolding that supports student after-class learning. How to deploy educational technology as a measure supporting primary students’ after-class learning? To address this question, we attempted to construct a data analytics-based self-regulated learning scaffolding model for primary students and verified its effects on student learning outcomes through experiments.  \nResearch Methodology  \nThis study selected two classes taught by the same teacher in X Primary School in Ningxia Province to conduct a control experiment. One of the classes was randomly chosen as the experimental group and required to use the scaffolding model to carry out self-regulated after-class learning; Learning data were also collected from the control group, but no intervention was imposed on it. The le","cbCaicKOQqVcfKh0","https://ap.wps.com/l/cbCaicKOQqVcfKh0","pdf",203218,"English","# Introduction\n# Research Methodology\n# Research Findings\n## The Effect of the Scaffolding Model on Learning Outcomes in Student Self-Regulated Study\n## The Effect of Scaffolded Self-Regulated Learning on Student Learning Strategies","[{\"question\":\"What is the main purpose of this study?\",\"answer\":\"To build a data analytics-based self-regulated learning scaffolding model for primary students and test its effects in teaching practice.\"},{\"question\":\"How was the experiment conducted?\",\"answer\":\"Two classes taught by the same teacher were selected in an X Primary School in Ningxia, with one class randomly assigned as the experimental group and the other as the control group.\"},{\"question\":\"What improvements were observed in learning outcomes?\",\"answer\":\"The experimental group’s average post-test score was higher than the control group’s, with a significant difference, and medium-to-lower level students improved while high-achieving students declined.\"}]","An Empirical Study on the Data Analytics-based Self-regulated Learning Scaffolding Model for Primary Students | PDF",1792080539,13]