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Digital Modules in Research Team-Based Learning in Higher Education: A Theoretical Review Filma Alia Sari; Ahmad Fauzan; Oriza Candra; Muhammad Yogi Riyantama Isjoni; Aulia Apriani; Yola Yolanda
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.8700

Abstract

The integration of digital modules in higher education has become increasingly vital in enhancing student engagement, collaboration, and critical thinking, especially in post-pandemic learning environments. Research Team-Based Learning (RTBL) is a collaborative pedagogy that emphasizes problem-solving, peer interaction, and research-based inquiry. This theoretical review explores the role of digital modules in supporting RTBL by synthesizing findings from 15 international open-access studies published between 2020 and 2025. Using a narrative review method guided by PRISMA procedures, studies were selected from reputable databases (e.g., DOAJ, PLOS ONE, Frontiers) based on criteria including relevance to RTBL and use of digital instructional materials. Thematic analysis identified four dominant themes: student engagement, digital literacy, instructional design, and post-pandemic implications. Findings indicate that digital modules enhance the effectiveness of RTBL by providing flexible access to learning materials, promoting active participation, and enabling peer-to-peer interaction. However, their impact is highly dependent on students’ digital literacy, the quality of instructional design, and institutional infrastructure. Poorly designed modules or limited digital skills can hinder collaborative learning outcomes. This review concludes that digital modules are not merely supplementary tools but essential enablers of RTBL success. Their implementation requires a balanced strategy that integrates pedagogical, technological, and institutional support. Future research should explore longitudinal impacts, AI-driven instructional feedback, and cross-cultural adaptations to optimize the use of digital modules in collaborative higher education settings.
Synthesizing Determinants of E-Learning Continuance Intention: A Meta-Analysis and Weight Analysis Hardisem Syabrus; M.Yogi Riyantama Isjoni; Aulia Apriani; Maha Rani
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.6591

Abstract

Understanding the factors influencing students' intention to continue using e-learning platforms is critical for sustaining digital education, especially in the post-pandemic era. While individual studies provide varying insights, a comprehensive synthesis is needed to clarify the most influential predictors of E-Learning Continuance Intention (ECI). This study conducted a systematic meta-analysis and weight analysis of 14 predictor variables related to ECI. Relevant peer-reviewed quantitative studies published between 2005 and 2022 were retrieved from Google Scholar. Inclusion criteria focused on empirical studies reporting correlation coefficients between ECI and its predictors. Meta-analysis was performed using Comprehensive Meta-Analysis Software, while weight analysis was applied to assess predictor significance based on frequency and strength of tested relationships. The findings identified Perceived Usefulness, Satisfaction, and Perceived Playfulness as best predictors, supported by both high correlation values and consistent significance across studies. Experimental predictors such as User Perception and Utility Value showed strong correlations but limited testing frequency. Four predictors, including Attitude and Social Influence, demonstrated lower predictive strength. Notably, Experiential Learning showed no significant correlation with ECI in either analysis. This study contributes to theoretical development by confirming and refining key constructs within the Expectation-Confirmation Model (ECM) in the e-learning context. The results provide practical implications for designing effective e-learning environments and highlight areas for future research, including underexplored or context-dependent predictors.