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Toward a Synergistic Model of Outcome-Based Education and Deep Learning: Addressing Alignment Gaps, Teacher Training, and Assessment Practices Muchammad Ibnu Muzakir; Muhammad Mamduh Nuruddin; Ali Muhlisin
At-Tathbīq: Journal of Education and Learning Vol. 2 No. 1 (2026): At-Tathbīq: Journal of Education and Learning
Publisher : CV. Kayaswara

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Abstract

Purpose of the Study: This study aims to develop a synergistic model integrating Outcome-Based Education (OBE) and deep learning in higher education to address persistent challenges in curriculum alignment, teacher readiness, and assessment practices. The research examines the interrelationships among Course Learning Outcomes (CLOs), Program Learning Outcomes (PLOs), teacher pedagogical competencies, and assessment models to formulate a comprehensive framework that enhances instructional quality and student learning outcomes. Methodology: This research employs a Systematic Literature Review (SLR) in accordance with the PRISMA protocol. Data were collected from Scopus and Web of Science-indexed scholarly articles published between 2015 and 2025, supplemented by seminal theoretical works. From an initial pool, 47 eligible studies were selected and thematically analyzed using open and axial coding to identify integration patterns across the curriculum design, teacher training, and assessment dimensions. Main Findings: Three critical dimensions emerged as foundational for successful OBE-deep learning integration: first, alignment between CLOs and PLOs requires a quantitative mapping matrix supported by Content Beyond the Syllabus initiatives; second, teacher training must prioritize generative pedagogical strategies and assessment literacy; third, a dynamic assessment model integrating process, summative, social, and value-added evaluations is essential. The synergistic model extends Biggs's constructive alignment by incorporating adaptive personalization and continuous feedback dimensions, which are enabled by digital technology. Novelty/Originality of this Study: This study presents a comprehensive, synergistic model that integrates OBE and deep learning paradigms into a unified framework, addressing integration gaps that have been previously unexplored in the literature. It extends constructive alignment theory by incorporating adaptive personalization and technology-enabled continuous feedback loops, and synthesizes evidence from recent international publications to provide a theoretical foundation for future empirical research in higher education curriculum reform.