BRIDGE model and relational-restorative approach for deep learning competency in Sekolah Rakyat

Aan Komariah; Suryadi Suryadi; Dedy Achmad Kurniady; Cucun Sunaengsih

doi:10.17509/dedicated.v4i2.781

Authors

Aan Komariah Universitas Pendidikan Indonesia
Suryadi Universitas Pendidikan Indonesia
Dedy Achmad Kurniady Universitas Pendidikan Indonesia
Cucun Sunaengsih Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.17509/dedicated.v4i2.781

Keywords:

BRIDGE Model, deep learning, relational-restorative approach, Sekolah Rakyat, teacher professional development

Abstract

Sekolah Rakyat serves children from underprivileged households in vulnerable social environments, requiring teachers to strengthen their professional competencies in accordance with Indonesia's national immersive learning policy (Permendikdasmen Nomor 13 Tahun 2025). This program aims to strengthen teachers' competencies in designing immersive learning through the BRIDGE Model (Building Resilient and Integrated Development for Governance Excellence). This integrated framework addresses student social polarization and teacher pedagogical development simultaneously. This program was implemented because external factors led students to form cliques that disrupted class unity, and because a substantial gap existed between teachers' aspirations and their capacity to implement immersive learning. The program design uses a research-based community service with participatory action research methodology involving 12 teachers from 4 mentoring groups as subjects through three sequential phases: 1) Online conceptual preparation; 2) Face-to-face practical mentoring; and 3) Consolidation of coaching clinic governance. Self-assessment on five sub-competencies serves as the main instrument. The program resulted in an overall average increase. Group 4 recorded the highest increase, as well as a decrease in scores for three participants who initially had high scores, which was interpreted as a Dunning-Kruger recalculation indicating deeper pedagogical awareness of the authentic complexities of deep learning practices.

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