Incorporating Learners’ Digital Trace Data into Self-Regulated Learning Research

Abstract

ABSTRACT Being able to self-regulate is critical for students to succeed in online learning due to the isolated nature of online learning. With the development of technology and online education, learners’ digital trace data can be collected and used to improve teaching and learning. Based on a comprehensive literature review of existing self-regulated learning models and measurements and related research, this paper proposed a comprehensive theoretical framework for self-regulated learning research in online learning environments by incorporating learners’ digital trace data from learning management systems. The theoretical framework provides a fundamental understanding for interpreting and the use of digital trace data for educational research.