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This Author published in this journals
All Journal Journal Mobile Technologies (JMS) International Journal of Technology and Modeling
Kumar, Ravi
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Leveraging Interactive Mobile Technologies for Enhanced Learning Outcomes A Systematic Review Kumar, Ravi; Sharma, Priya
Journal Mobile Technologies (JMS) Vol. 3 No. 1 (2025): February
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/jms.v3i1.540

Abstract

The growing integration of mobile technologies has reshaped how learning is designed, experienced, and evaluated in modern education. This systematic review synthesizes findings from 78 empirical studies published between 2015 and 2024 to examine how interactive mobile tools—such as adaptive systems, simulations, gamification, and augmented reality—affect measurable learning outcomes. The analysis followed the PRISMA framework and focused on peer-reviewed research that employed experimental or quasi-experimental designs. Results indicate that mobile learning yields moderate yet consistent improvements in knowledge acquisition, motivation, and skill development, with stronger effects observed in STEM and health-related subjects. Adaptive systems and simulation-based applications were found to be the most effective, while gamification and augmented reality produced mixed outcomes depending on instructional design quality. The review also reveals that pedagogical alignment, teacher readiness, and institutional support play a decisive role in determining success. Technology by itself does not ensure learning gains; rather, meaningful results arise when digital tools are purposefully embedded into curriculum design and supported through professional training. Despite methodological and contextual limitations in existing studies—such as short intervention durations and limited geographic diversity—the evidence supports a cautiously optimistic view: mobile learning can complement traditional instruction when guided by sound pedagogy, coherent assessment strategies, and equitable access.
A Survey on Object Detection in Dynamic and Complex Environments Soni, Ritu; Kumar, Ravi; Jain, Sheetal
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.134

Abstract

Object detection has become a cornerstone of computer vision, with applications ranging from autonomous driving and robotics to surveillance and augmented reality. While substantial progress has been made in controlled and static settings, real-world environments often pose significant challenges due to dynamic backgrounds, occlusions, illumination variations, and cluttered scenes. This survey provides a comprehensive review of recent advancements in object detection specifically tailored for dynamic and complex environments. We classify existing approaches based on their core methodologies, including traditional feature-based techniques, deep learning models, and hybrid frameworks. Key challenges such as real-time performance, adaptability to environmental changes, and robustness to motion are discussed in depth. Furthermore, we analyze benchmark datasets and evaluation metrics commonly used in this domain, highlighting their limitations and suggesting improvements. Finally, we explore emerging trends and future directions, including the integration of spatiotemporal modeling, sensor fusion, and domain adaptation strategies. This survey aims to serve as a valuable reference for researchers and practitioners seeking to develop or apply object detection systems in real-world, unpredictable environments.
Co-Authors Jain, Sheetal Sharma, Priya Soni, Ritu