The Effect of Deep Learning and Love-Based Curriculum on Students' HOTS at MA Hikmah Kamilah Brebes

Abstract

Digital transformation brings changes to the way students learn and think, but the learning process in a number of schools still faces low student participation, reflective skills, and empathy. Learning that is oriented towards mastery of academic material also tends to position students as recipients of information so that critical thinking skills and the connection of knowledge to social life have not been developed optimally. This study aims to analyze the effect of the implementation of Deep Learning combined with the Love-Based Curriculum (KBC) on students' Higher Order Thinking Skills (HOTS) in Islamic Religious Education (PAI) learning at MA Hikmah Kamilah Brebes and identify differences in results compared to conventional learning. The study used a quantitative descriptive approach by comparing HOTS achievements between classes implementing Deep Learning–KBC and conventional classes. The results showed that the Deep Learning–KBC class obtained an average score of 21.1, while the conventional class obtained an average score of 14.2, with a difference of 6.9 points. In the Deep Learning–KBC class, 40% of students were in the very high category and 60% were in the high category. In contrast, in conventional classes, 50% of students were in the low category and 20% in the very low category. These findings indicate that the integration of Deep Learning and KBC provides more optimal HOTS achievement compared to conventional learning. The reflective, contextual, and humanistic approach helps students understand the material in depth, develop critical thinking, and connect knowledge with social experiences. This study provides an alternative Islamic Religious Education learning that integrates HOTS development with humanitarian values ​​and real-life relevance to support 21st-century learning competencies.