Integration of Deep Learning Concepts and Modern Education Evaluation: A Comparative Study of Indonesia and Singapore

Abstract

This study aims to analyze the integration of deep learning concepts in modern education evaluation systems through a comparative study between Indonesia and Singapore. The study is grounded in the assumption that the level of deep learning integration in educational evaluation is influenced by policy readiness, technological infrastructure, and human resource competence. A qualitative comparative approach was employed by analyzing policy documents, international reports, and relevant scientific literature. The findings indicate that Singapore has systematically implemented deep learning to support adaptive and process-oriented learning evaluation. In contrast, Indonesia’s evaluation system remains largely focused on learning outcomes with limited technological integration. These results reveal a structural gap in the implementation of modern educational evaluation and highlight the need to reformulate Indonesia’s evaluation policies toward a more data-driven and learning-oriented approach