Making Deep Learning Happen
Technology, TPACK, and Contextual Adaptation among PPG Students
DOI:
https://doi.org/10.31004/anthor.v5i5.1422Keywords:
Deep Learning, Teacher Professional Education Program, TPACK, Technology Integration, Adaptive CapacityAbstract
The application of Deep Learning in teacher education requires prospective teachers not only to understand the principles of meaningful learning but also to be able to apply them in technology-based teaching practices. This study aims to analyze how students in the Teacher Professional Education Program (PPG) understand and apply Deep Learning, including the challenges, adaptive strategies, and contextual conditions that influence its application. This study employed a qualitative case study design with purposive participant selection. Data were collected through semi-structured interviews, classroom observations, and document analysis, and were subsequently analyzed using iterative coding and thematic analysis with source triangulation. The results indicate that PPG students understand Deep Learning as meaningful, learner-centered, contextual, and promoting active engagement; however, this understanding is not always consistently reflected in classroom practice. Implementation faces challenges such as teacher-centered teaching habits, time constraints, difficulties in designing meaningful activities, infrastructure and connectivity issues, and limitations in the pedagogical integration of technology. The findings also indicate that implementation is influenced by the interaction between teacher readiness, Technology-Pedagogy-Content Knowledge (TPACK), contextual conditions, and adaptive capacity. Students responded to these challenges through strategy modification, reflection, collaboration, and the use of feedback from mentors. This study produced a Contextual-Adaptive Framework that views the implementation of Deep Learning as an iterative and contextual process linking environmental conditions, readiness and TPACK, pedagogical-technological integration, Deep Learning practices, as well as reflection and adaptation.
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