Abstract:

This literature review explores the significance, theoretical frameworks, methodologies, evolution, applications, challenges, and future trends of Big Data in education. Big Data has emerged as a powerful tool in education, enabling the collection, storage, and analysis of vast amounts of educational data. Theoretical discussions have emphasized the role of data analytics in educational settings, including learning analytics, educational data mining, and predictive analytics. The review discusses the historical background of Big Data adoption in education, major milestones, and technological advancements that have shaped its evolution. Various applications of Big Data in education are presented, such as personalized learning, learning analytics, educational data mining, assessment improvement, and teacher professional development. The benefits of Big Data in education include improved student learning outcomes, enhanced teaching effectiveness, evidence-based decision-making, identification of at-risk students, and data-driven policy development. However, challenges concerning data privacy and security, ethical considerations, bias in data analysis, data integration, and staff capacity building should be addressed. Successful case studies demonstrate the transformative potential of Big Data in education. Implications for educational policy and practice are highlighted, emphasizing the importance of data privacy, professional development, and data-driven strategies for improved student support and learning experiences. The future of Big Data in education looks promising with emerging technologies like artificial intelligence, Internet of Things, and augmented reality offering new opportunities for personalized learning and evidence-based decision-making. To fully realize these opportunities, ethical considerations, bias mitigation, and continuous research and practice will be essential.

Keywords:

Big Data, Education, Data Analytics, Personalized Learning, Learning Analytics, Educational Data Mining, Predictive Analytics, Data Privacy.