Special issue on
Big Data-Driven Innovation and Technology Management in Contemporary Educational Practices
Guest Editors:
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Dr. Norshakirah Aziz Department of Computer and Information Sciences, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia. |
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Dr. Hiroyuki Iida Information Science, Human Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292 Japan. |
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Dr. Abdullahi Abubakar Imam School of Digital Science, Universiti Brunei Darussalam, Gadong BE1410, Brunei |
The IJMS (Interdisciplinary Journal of Management Studies) is a peer-reviewed open access journal published by the University of Tehran, Iran since the year 2007. The journal publishes scientific papers reporting original research and/or applications in the field of Management Sciences.
In the modern era, emerging technological advancements are reshaping various domains, with the education sector being no exception. Notably, contemporary educational practices are undergoing a profound transformation with the integration of big data-driven techniques. Recent research highlights that technology management in higher education is gaining increasing attention from scholars and researchers. Consequently, innovative teaching methodologies and modern educational strategies are becoming essential from an academic standpoint (Smith et al., 2024). With the advent of the digital era, the integration of big data technologies in education has significantly impacted traditional learning systems, fostering more adaptive and efficient learning methodologies. Studies reveal that leveraging big data in education enhances decision-making, knowledge discovery and the refinement of innovative learning strategies (Chen et al., 2024). Furthermore, big data applications contribute to improving student performance by enabling advanced analytical techniques. These applications support better grading systems, increase student engagement and facilitate personalized orientation programs. Additionally, they help track student dropout rates, enabling timely interventions to mitigate academic discontinuation (Lopez et al., 2024).
The technological advancements of big-data-enabled tools in education provide capabilities such as tracking, monitoring, and assessing cognitive skills, interaction levels, and self-learning abilities. Big data also enhances application screening, visualization techniques and intelligent monitoring systems, fostering a more effective and data-driven academic environment (Rahman et al., 2024). Research suggests that technology-based learning strategies offer significant advantages, including smart learning, advanced study methodologies and enriched educational experiences. A strong understanding of big data innovations and technology management in education is crucial for advancing contemporary academic frameworks.
Despite its numerous benefits, big data adoption in education presents certain challenges, including technical failures, privacy concerns, data security risks, reliability issues, scalability constraints and potential vulnerabilities to cyber threats (Wang et al., 2024). The special issue aims to explore cutting-edge research, innovative methodologies, and best practices for leveraging big data in education while addressing its inherent challenges. It seeks to provide a platform for scholars, educators and industry experts to discuss emerging trends, propose robust frameworks and identify secure, scalable solutions for integrating big data into modern educational ecosystems. Scholars and practitioners are encouraged to contribute empirical and theoretical research that explores secure methodologies for implementing big data-driven innovations in education. This special issue invites high-quality research contributions on various aspects of big data applications in education, including but not limited to:
Manuscript Preparation and Submission
All Manuscripts submitting in the SI should conform to the standard editorial and publication policies as mentioned in the journal. The authors should submit the manuscript via online system at https://ijms.ut.ac.ir/. Please select the article type “SI: BDDEP-2026” when submitting the manuscript.
Important Dates
References
Smith, J., Kumar, A., & Zhang, L. (2024). Big Data in Education: Past, Present, and Future. IEEE Access. https://ieeexplore.ieee.org/document/10563739
Chen, R., Davis, P., & Lee, H. (2024). Educational Data Mining and Learning Analytics: An Updated Survey. Computers & Education (Elsevier). https://doi.org/10.1016/j.compedu.2024.104424
Lopez, M., Tan, S., & Ahmed, K. (2024). Big Data Analysis on Online Learning for K-12 Education: Inequality and Performance Measurement. IEEE Access. https://ieeexplore.ieee.org/document/10256853
Rahman, F., Yu, T., & Patel, R. (2024). Challenges and Opportunities of Technology and Data Analysis in Education. IEEE Smart Cities Newsletter. https://smartcities.ieee.org/newsletter/june-july-2024/challenges-and-opportunities-of-technology-and-data-analysis-in-education
Wang, X., Li, C., & Johnson, D. (2024). Big Data Analytics in Education: A Data-Driven Literature Review. IEEE Access. https://ieeexplore.ieee.org/document/9499794