Navigating Deep Learning Pedagogy in Rural Classrooms: A Qualitative Study on Teacher Readiness and Innovation in Indonesian Elementary Schools
Abstract
Purpose of the study: The integration of digital technology in primary education has become increasingly urgent in the era of 21st-century learning transformation. However, teachers’ readiness to implement deep learning approaches and instructional innovation remains a challenge, particularly in non-urban areas such as Sumenep Regency.
Methodology: A qualitative descriptive approach was employed, involving six purposively selected teachers as primary data sources. Data were collected through classroom observations and structured interviews and then analyzed using the Miles and Huberman model, which includes data reduction, data display, and conclusion drawing.
Main Findings: The findings reveal that teachers’ conceptual understanding of deep learning remains limited, and its classroom application has not reached a transformative level. Teacher readiness is influenced by insufficient training, inadequate infrastructure, weak institutional support, and varying levels of self-efficacy. Systemic barriers such as limited technological access and lack of supportive school policies hinder also implementation efforts.
Novelty/Originality of this study: This study uniquely examines teacher readiness for deep learning-oriented instruction in an underrepresented context—rural elementary schools in Indonesia. Unlike previous research that predominantly focuses on urban or secondary education settings, this study captures the real-world constraints and opportunities for digital transformation in low-resource environments. It also broadens the conceptual framing of “deep learning” beyond technology, integrating pedagogical depth and reflective teaching practices. The implications highlight the urgency of designing context-based, practice-oriented teacher training programs and developing supportive policies that enable sustainable digital pedagogy.
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