Self-regulated Learning Recognition and Improvement Framework

Abstract

Self-regulated learning (SRL) is a learning approach whereby learners actively set learning goals, then monitor, control their learning progress, and finally reflect on their learning performance. In the last three decades, SRL has drawn attention not only from researchers but also from schools and universities that aim to equip their learners with self-study ability. With the development of distance learning and e-learning technologies, SRL has become a crucial ability for learners. Profoundly, in the last several months, the strike of COVID-19 has isolated students, teachers, and dramatically challenged the current learning and teaching approaches; COVID-19 seems to force learners to self-regulate their own study without options. Understanding SRL maturity is necessary for the educational growth and knowledge fulfillment of individuals. Although there have been increasing studies and models about how SRL works and is measured, it still remains a challenge for research on the principles whereon SRL exists, operates and the foundation for SRL intervention for improvement. Aiming for these principles, we propose the SRL Recognition and Improvement Framework to support the process of recognizing one’s SRL maturity level and improving SRL ability. This framework is a set of educational principles, models, and factors based on which formulas, techniques, and environments for supporting SRL recognition and improvement are developed. The framework is constructed on the foundation of the theory of metacognition and cognition, the philosophical habit of the mind, and existing SRL models and measurement methods. We also demonstrate the experiment design for validating the framework and conducting empirical studies.



Author Information
Minh-Tuan Tran, Japan Advanced Institute of Science and Technology, Japan
Shinobu Hasegawa, Japan Advanced Institute of Science and Technology, Japan

Paper Information
Conference: ACE2020
Stream: Adult

This paper is part of the ACE2020 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window

Video Presentation


Comments & Feedback

Place a comment using your LinkedIn profile

Comments

Share on activity feed

Powered by WP LinkPress


Share this Research

Posted by amp21