Big Data Analytics in Education: Challenges, Opportunities, and Personalized Learning Approach

In recent years, the realm of Big Data has witnessed an exponential surge in the volume and diversity of data, characterized by its sheer scale, both structured and unstructured formats, and its relentless growth. This surge is largely attributed to the proliferation of internet connected devices, generating a torrent of data daily. Concurrently, the nature of this data is evolving, presenting organizations with the challenge of efficiently capturing and processing it. Traditional technology, architectures, and analysis methods are proving inadequate to handle this deluge of data. Consequently, organizations are compelled to redefine their approach to data, encompassing planning, and governance, management, processing, and reporting, in order to unlock the full potential of Big Data. Unveiling insights and knowledge from this vast data landscape poses a formidable challenge. As the applications of Big Data continue to proliferate, so do the associated challenges. To harness higher productivity and more efficient data utilization, it is imperative to undertake enterprise-level analysis of Big Data. The educational landscape has undergone a transformation in the way people acquire knowledge, owing to advancements in information technology. Fragmented learning, as an informal learning method, has gained popularity for acquiring new skills and expertise. Academic institutions generate an extensive and heterogeneous dataset, prompting academic leaders to seek ways to extract maximum value from it for better decision-making. Beyond the sheer volume of data, the diverse data structures (ranging from structured to semi-structured and unstructured) further complicate academic operations and daily decision-making processes. The prevalence of Big Data in educational settings has given rise to innovative data-driven techniques aimed at enhancing informed decision-making and improving educational efficacy. These techniques leverage digital traces of student behavior, enabling a more scalable and detailed understanding of learning processes, which was previously cost-prohibitive using traditional data sources and methods. In this context, one of the notable developments is the use of digital traces of student behavior for educational data analysis. Unlike traditional data sources, which were often expensive to obtain and limited in granularity, digital traces offer a more scalable and fine-grained understanding of how students learn. The Big Data era has ushered in a new paradigm of data management and analysis, touching nearly every facet of our lives. Within the realm of education, the impact is profound, as institutions grapple with the challenges and opportunities presented by the data deluge. Data-driven decision-making, powered by digital traces of student behavior, promises to enhance the learning experience, optimize educational processes, and transform education into a dynamic and personalized journey. As we continue to navigate the complexities of Big Data, it is imperative to recognize its potential as a catalyst for positive change in education and society as a whole. This study presents a solution for fragmented learning within a data-rich environment, proposing personalized subject recommendations based on factors such as geographical location, gender, and district of residence. This framework is poised to play a pivotal role in fostering a society that values lifelong learning. In summary, this thesis delves into the challenges and opportunities presented by the Big Data era, particularly in the context of education. It explores novel data-driven strategies to inform decision-making and enhance learning outcomes, recognizing the transformative potential of Big Data in shaping our educational landscape