An Empirical Study on the Data Analytics-based Self-Regulated Learning Scaffolding Model for Primary Students

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 analytics-based 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 after-class learning can help enhance learning outcomes and develop student learning strategies.