The Role of Self-Directed Learning in Promoting Deep Learning Processes: A Systematic Literature Review

Abstract

In the realm of current educational research, deep learning is extensively acknowledged as a pivotal approach to enhancing the holistic development of students' capabilities. However, the effective facilitation of deep learning, particularly within diverse educational settings, continues to pose a significant challenge. Notably, the contribution of Self-Directed Learning (SDL) within the deep learning process and its integration into educational practices have yet to be comprehensively explored. Against this backdrop, this study engages in a systematic literature review to investigate the promotive effect of SDL on deep learning, and to analyze how strategies and environments conducive to self-directed learning can bolster the deep learning process. This article, grounded in Self-Determination Theory (SDT) and metacognitive theory, delves into the synergistic impact of self-regulation, interest and motivation, reflection and evaluation, as well as the effective application of technology on deep learning. Through an integration of existing literature, this study reveals how self-directed learning significantly enhances deep learning by fostering students' active engagement, self-management, and the development of metacognitive skills. The analysis underscores the critical role of students' personal interests and intrinsic motivation, as well as reflective and evaluative activities in deepening knowledge comprehension. It also highlights how the effective utilization of technology supports self-directed learning, further facilitating the realization of deep learning. Although the positive relationship between Self-Directed Learning (SDL) and deep learning has been confirmed, challenges remain in effectively integrating these two modes of learning across diverse educational contexts. Future research should see educators and researchers continuing to explore innovative teaching strategies and learning environments to facilitate the effective amalgamation of SDL and deep learning.