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All Journal International Transactions on Education Technology (ITEE)
Ismail, Farid Bin
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Control & Systems Engineering Social Sciences

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Exploring the Frontier of Data Science: Innovations, Challenges, and Future Directions Ismail, Farid Bin; Xuan, Alvin Teo Zi; Rusilowati, Umi; Williams, James
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.594

Abstract

Data science, an interdisciplinary field, has profoundly transformed our understanding and utilization of data across diverse sectors such as healthcare, finance, marketing, and transportation. With the rapid advancements in computational power and the exponential growth of data from digital sources, sophisticated methodologies and tools have emerged, enabling deeper insights and more informed decision-making. This paper explores the latest innovations in data science, focusing on advancements in machine learning algorithms, big data technologies, and data visualization tools. It highlights the development of cutting-edge techniques that enhance predictive accuracy, optimize resource allocation, and improve operational efficiencies. Additionally, we address the key challenges faced by practitioners, including ensuring data quality and management, navigating ethical and privacy concerns, and bridging the skill gap within the workforce. By examining these aspects, the paper provides a comprehensive overview of the current state of data science and its implications for future research and application. The insights gathered aim to guide researchers and professionals in leveraging data science advancements while addressing the inherent challenges to maximize the potential benefits across various industries.
A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments Sutarman, Asep; Williams, Jack; Wilson, Daniel; Ismail, Farid Bin
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.663

Abstract

This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.
Co-Authors rusilowati, umi Sutarman, Asep Williams, Jack Williams, James Wilson, Daniel Xuan, Alvin Teo Zi