Analisis Kompetensi Pembelajaran Anak Usia Dini Melalui Inovasi Pedagogik Berbasis Deep Learning Untuk Meningkatkan Motivasi Belajar

Abstract

Early childhood education requires learning approaches that foster motivation through meaningful and enjoyable experiences, making pedagogical innovation essential in the digital era. This study aims to analyze early childhood learning competencies through the implementation of deep learning–based pedagogical innovations to enhance learning motivation at El-Azzam Kindergarten, Pekanbaru. The research employed a qualitative descriptive method, with data collected from one Grade B teacher through classroom observations and semi-structured interviews. Data analysis was conducted using the interactive model of Miles and Huberman, encompassing data reduction, data display, and conclusion drawing. The findings indicate that teachers possess adequate pedagogical, professional, social, and personal competencies; however, the use of interactive digital media remains limited. The application of deep learning–based pedagogical innovation, which emphasizes meaningful, reflective, and contextual learning, was found to increase children’s active participation, intrinsic learning motivation, and emotional engagement in learning activities. Moreover, this approach encourages teachers to become more reflective, creative, and responsive to individual learner characteristics. The practical implications of this study suggest that integrating deep learning principles with educational technology can serve as an effective strategy to improve the quality of early childhood education and support the continuous development of teacher competencies