Open AccessData Mining and Knowledge Discovery in Big Data for Decision Making in Higher Education
Author(s) -
Hanna Mohammad Said
Publication year - 2021
Publication title -
bioscience biotechnology research communications
Language(s) - English
Resource type - Journals
eISSN - 2321-4007
pISSN - 0974-6455
DOI - 10.21786/bbrc/14.4.93
Subject(s) - big data , computer science , data science , interoperability , scarcity , data quality , quality (philosophy) , knowledge extraction , intelligence analysis , knowledge management , artificial intelligence , engineering , computer security , data mining , world wide web , metric (unit) , philosophy , operations management , epistemology , economics , microeconomics
Artificial intelligence and data mining plays a fundamental role in improving the intelligence of education through special standards for improving teaching quality, better learning experience, predictive teaching, assessment method, effective decision-making, and improved data analysis. BD (Big Data) are also used to assess, detect, and anticipate decision-making, failure risk, and consequences to improve decision-making and maintain high-quality standards. According to the findings of this study, certain universities and governments have adopted BD to help students transition from traditional to smart digital education. Many obstacles remain in the way of complete adoption, including security, privacy, ethics, a scarcity of qualified specialists, data processing, storage, and interoperability. Learning today is getting smarter, thanks to the rapid development of the use of data and knowledge for big data analysis. Besides delivering real-world knowledge discovery applications, specialized data mining methodologies, and obstacles have real-world applications. Therefore, this article aims to explain the current concept of an intelligent learning environment in higher education. It explores the main criteria, and presents evaluation methods through the use of the proposed model.