Big Data in Education Sector

Universities either public or private and its colleges enroll thousands of students into various courses or programs every year. They collect information from students at the time of admissions and store the same in computers. Understanding the benefits of data is essential from business point of view. Data can be used for classifying and predicting the students behaviour, performance, dropouts as well as teachers' performance. Therefore, this paper examines the role of data mining in an education sector. In addition, lays emphasis on application of data mining that contribute to offer competitive courses and improve their business. Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.