Enhancing PGMI Students’ Pedagogical Competence Through Deep Learning-Based Lesson Plan Design - Ali Mahsun; Jasminto Jasminto; Siti Rofi'ah; Maidatus Sa'diyah; Evita Widiyati; Imam Muslih (Universitas Nurul Jadid, 2025)
This community service program aims to enhance the pedagogical skills of students in the Primary School Teacher Education (PGMI) Study Program in designing Deep Learning–based lesson plans (RPP). The program was implemented to address the urgent need for prospective teachers to meet the demands of the Merdeka Curriculum, which requires meaningful, student-centered, and competency-based learning. Many PGMI students still struggle to integrate Deep Learning principles into lesson planning, especially in formulating measurable objectives, selecting active strategies, and designing higher-order thinking assessments. The program involved 15 PGMI students as primary participants. Using a Participatory Action Research (PAR) approach, the program consisted of three stages: needs analysis, training, and mentoring. The needs analysis identified gaps in students’ understanding of lesson plan components. The training introduced theoretical foundations and practical models of Deep Learning–oriented lesson planning, while the mentoring phase provided guided practice in developing complete RPPs. Students contributed actively through reflective discussions, prototype development, and peer feedback. The results show a notable improvement in students’ pedagogical competence, reflected in an increase from an average pretest score of 31.5% to a posttest score of 43.2%, indicating an 11.7% gain. Deep Learning in this program refers to a pedagogical approach emphasizing conceptual depth, problem-solving, reflection, and higher-order thinking. Overall, the program strengthened students’ readiness to become professional and adaptive teachers capable of designing learning aligned with the Merdeka Curriculum.