Assessing Big Datas Impact of Uncertainty from the Perspective of Core Education

With organizations like Shodhganga managing sizable academic data repositories, this study examines the growing problem of uncertainty in big data analytics from a regulatory and educational standpoint. Numerous industries, including healthcare, urban infrastructure, social media, finance, and education, are generating huge quantities of data as a result of the quick development of sensor networks, cyber-physical systems, and the Internet of Things (IoT). However, the accuracy and comprehension of this data are often compromised by noise, incompleteness, and inconsistencies. Accurate decision-making becomes even more challenging as data attributes—volume, variety, and velocity—increase due to the associated uncertainty. Artificial intelligence (AI) approaches, especially machine learning, natural language processing, and computational intelligence, have demonstrated better performance in terms of scalability and precision than traditional deterministic methods, which falter under such complexity. The study suggests a hybrid, multi-layered analytical framework to address these issues. This method combines neural networks for semantic-rich prediction, wrapper-based feature selection to reduce dimensionality, probabilistic modeling for informed classification, and fuzzy logic for handling subtle uncertainties. The real-world student-por.csv dataset is used to test this framework, and the results show that it can accurately classify educational outcomes with high F1-scores (85%), particularly for high-performing students. The study discusses advancement in theory and practice by providing an innovative approach which is sensitive to uncertainty and turns noisy educational datasets into intelligent perception for directing institutional policy and individualized learning plans.