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Big Data and its Application Areas in Higher Education


Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, India
 

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.
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Abstract Views: 296

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  • Big Data and its Application Areas in Higher Education

Abstract Views: 296  |  PDF Views: 175

Authors

Deepa Jose
MCA Department, Y.M.T College of Management, Kharghar, India

Abstract


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.

References