Deep Learning Implementation and Teacher Training Gaps in Schools

Abstract

The deep learning policy mandated under Regulation of the Minister of Primary and Secondary Education No. 13 of 2025 requires teachers to shift their instructional orientation from rote memorization toward understanding that is mindful, meaningful, and joyful. However, the policy has been rolled out nationwide amid persistent and uneven gaps in teacher training, leaving many educators unable to clearly distinguish deep learning from related approaches such as differentiated instruction or project-based learning. This study analyzes how deep learning is being implemented in education units and identifies the teacher-training gaps that constitute its principal obstacle. The study uses a library research method, reviewing current policy documents, journal articles, and relevant, up-to-date teacher-training reports. Findings show that field implementation largely remains at the stage of conceptual socialization and has not progressed to sustained technical mentoring, so that teachers tend to interpret deep learning superficially for instance, merely adding technology use or increasing student workload. This gap is compounded by teachers' limited digital literacy, a scarcity of concrete cross-subject application examples, and training programs that are not tiered to schools' actual needs, particularly in disadvantaged, remote, and frontier (3T) regions. The study concludes that the success of deep learning depends on strengthening continuous teacher training, providing post-training mentoring, and securing school-level policy support that gives teachers room to innovate incrementally.