Data-Driven Decision Making In Education: Role Of Big Data Technologies

Abstract

Personalized learning, improved accountability, and data-driven decision-making represent a few of the advantages of education datafication. It enables the modification of educational approaches to individual student requirements, hence improving educator effectiveness and the achievement of students. However, other academics suggest that datafication could contribute to excessive dependence on data, jeopardizing teacher competence and judgment. Ensuring ethical data usage in education is extremely important since the quality and nature of data accessible might restrict decision-making accuracy. Quality should be viewed as an ongoing improvement process rather than a static endpoint, with an emphasis on continuous progress rather than goal-oriented. The study presents a data-driven decision-making methodology to evaluate the relevance, acceptance, and application of emerging technologies like Augmented Reality (AR) to improve the standard of education. It discusses a user-centered technique for evaluating AR applications in education, emphasizing the influence of individual deviations on IT acceptability and implementation. AR is a contemporary technical innovation that can be employed for enhancing educational standards. Artificial intelligence (AI) and big data analytics are emerging fields which employ algorithms for assessing data and provide greater insight. The article presents a data-driven decision-making approach for educational institutions that leverages machine learning algorithms to analyze student data, rates of success, and curriculum development for management decisions. This study investigates the influence of big data on leadership at schools, with the goal of improving educational quality. Data were obtained from a variety of sources, including scholarly publications, books, and seminar papers. A library research approach was employed in conjunction with an interactive qualitative model that included data collecting, reduction, presentation, and conclusion drafting. The findings seek to enhance the application, positive aspects, and challenges associated with big data in educational administration.