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All Journal Journal Evaluation in Education (JEE) Indonesian Journal of Educational Development (IJED)
Sumarlin Sumarlin
STIKOM Uyelindo Kupang
Education Social Sciences Other

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Breaking Connectivity Barriers: B-Smart as an Innovative Low-Bandwidth Mobile Learning Solution for Underserved Communities Petrus Katemba; Sumarlin Sumarlin; Jimi Asmara; Remerta Noni Naatonis
Journal Evaluation in Education (JEE) Vol 7 No 2 (2026): April
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jee.v7i2.2769

Abstract

Purpose of the study: Access to digital learning resources remains a critical challenge in underserved communities, particularly those constrained by limited connectivity, inadequate infrastructure, and low-specification devices. This study aims to design, develop, and evaluate B-Smart, a low-bandwidth mobile learning application specifically engineered to bridge the digital learning gap in resource-limited educational environments. Methodology: A Design and Development Research (DDR) approach was employed, integrating an offline-first architecture, modular microlearning content, lightweight interface components, and data-efficient synchronization. Evaluation involved technical performance testing on low-end Android devices (1–2 GB RAM), usability testing using the System Usability Scale (SUS), pre-test and post-test learning assessments, and qualitative user feedback from 80 students and 15 teachers. Main Findings: B-Smart demonstrated reliable technical performance, with an average module loading time of 1.8 seconds, memory usage of 112 MB, weekly data consumption of 0.9–1.2 MB, and an offline access success rate of 98.7%. Usability evaluation yielded an SUS score of 82.4, while learning assessments revealed a mean post-test improvement of 24.6 points over pre-test scores, confirming significant knowledge gains across all user groups. Novelty/Originality of this study: These findings establish B-Smart as a novel, pedagogically sound, and technically efficient mobile learning solution tailored for low-bandwidth contexts. Unlike existing applications that depend on stable connectivity, B-Smart's offline-first, resource-efficient design ensures uninterrupted learning continuity in underserved regions. The study contributes a replicable development framework for scalable digital education initiatives, with practical implications for policymakers, educators, and developers seeking to advance equitable access to quality education in communities.
EduVa: Prototyping and testing AI-powered interactive LMS with adaptive modules and assessments Sumarlin Sumarlin; Skolastika Siba Igon; Remerta Noni Naatonis; Dewi Anggraini; Heni Heni
Indonesian Journal of Educational Development (IJED) Vol. 7 No. 2 (2026): August 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/ijed.v7i2.6253

Abstract

The rapid integration of artificial intelligence (AI) in higher education has increased the demand for adaptive Learning Management Systems (LMS) capable of delivering personalized learning at scale. However, empirical studies covering the full lifecycle of designing, prototyping, and testing AI-powered LMS remain limited, particularly in developing regions. This study aimed to design, prototype, and test EduVa LMS (Education Virtual Assistant), an AI-powered interactive LMS with adaptive modules and AI-driven assessments, implemented across ten private universities in East Nusa Tenggara, Indonesia. A Design Science Research (DSR) approach integrated with the ADDIE model guided the development process. A total of 360 participants were involved, including experts, students, and lecturers. Validation results showed a Content Validity Index (CVI) of 0.80, confirming content validity. Usability testing using the System Usability Scale (SUS) yielded mean scores of 82.4 (students) and 85.8 (lecturers). Adaptive performance analysis indicated 82.1% assessment accuracy, 3.7 content adjustments per session, and an 87.9% completion rate. A strong correlation (r = 0.81, p < .01) was found between AI assessments and learning objectives. The findings demonstrate that the EduVa LMS is valid, usable, and effective for implementing adaptive learning.
Co-Authors Dewi Anggraini Heni Heni Jimi, Asmara Petrus Katemba Remerta Noni Naatonis Remerta Noni Naatonis Skolastika Siba Igon