Optimalisasi Kompetensi Guru dalam Pengimplementasian Pendekatan Deep Learning Berbasis Cognitive Load Theory (CLT) dalam Implementasi Kurikulum Merdeka - Arti, Yuli; Natadiwijaya, Ismail Fikri; Anjarsari, Putri; Prasetyo, Zuhdan Kun (Universitas Pahlawan Tuanku Tambusai, 2025)
Research on science learning using the Cognitive Load Theory (CLT) approach has shown positive impacts on students, particularly in reducing cognitive load and improving learning outcomes. Toward the end of 2024, deep learning emerged as a central issue in education, proposed as a strategy to enhance the quality of education in Indonesia. Therefore, the development of deep learning instructional materials needs to consider students’ cognitive load. Previous studies confirmed that CLT-based approaches are effective; however, preliminary survey results through teacher interviews revealed that many teachers still lack an understanding of CLT, including how to design and implement CLT-based instruction. Furthermore, science teaching modules that integrate deep learning remain limited, and examples provided in the Merdeka Mengajar Platform (PMM) are still scarce. Considering the importance of CLT in science learning and the limited teacher knowledge, training is essential to optimize teachers’ competence in developing deep learning approaches grounded in CLT. This activity proved effective in enhancing teachers’ implementation skills.