The Influence of Deep Learning Strategies on Autonomy of Bachelor of Early Childhood Education Students

Abstract

 Student autonomy is a crucial factor in early childhood education, yet many students struggle with self-regulated learning and independent decision-making. This study explored the influence of deep learning strategies on autonomy among early childhood education students in a local college in Region XI, Philippines, utilizing a nonexperimental quantitative research design, specifically correlation and regression analysis. Data was gathered through a census approach from 100 students using validated questionnaires measuring deep learning strategies and autonomy levels. Findings revealed a strong positive correlation between deep learning strategies and autonomy, with regression analysis indicating that deep learning strategies significantly predict autonomy. These results affirm Self-Determination Theory (SDT, 1985), which highlights the role of intrinsic motivation and self-regulation in fostering independent learning. However, external factors such as teacher support, institutional policies, and instructional methods also contribute to autonomy development. The study underscores the importance of integrating deep learning strategies into early childhood education curricula to enhance self-directed learning and academic success. Future research should examine additional mediating factors influencing autonomy and refine instructional approaches to support independent learning.