Review of Big Data on Student Information for Finding the Uncertainty in Higher Education Enrollment

Review of Big Data on Student Information for Finding the Uncertainty in Higher Education Enrollment

S. Krishnaveni (Periyar Maniammai University, Tamil Nadu, India), A. Satheesh (Periyar Maniammai University, Vallam, India) and E. Kannan (Veltech Dr. RR and Dr.SR Technical University, Tamil Nadu, India)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/IJGHPC.2015100102
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The student enrollment for higher study kept on decreasing continuously especially for the past five years. This is affecting the higher education institution drastically – threatening the very existence and sustenance leave alone their sustained growth. Why is this capricious global trend? What is the mitigating solution for the revival? The answers to these questions are being attempted to in this paper by making use of Big Data concept. The four phases of big data namely data generation, data acquisition, data storage and data analytics on various parameters of the student data set from Valdosta state university (VSU) are being adopted for the analysis. The main causes for the epidemic are academic stress, demography, co-student behavior and uncertainty about the future. This paper also address the open issues of big data that promotes the cross function of science and technology that triggers the thinking revolution.
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The recent approach of data mining has got great potentials to educational institutes. Jiaqu Yi (Jiaqu Yi, 2014) was tempered to find out the application of big data research in education field. His work has not only provided an efficient way of analyzing students’ learning skills and academic performance, but more importantly, teachers are able to modify the course content and school work for students based on their performance. For school principals and education authorities, the result also provides a good reference for designing education curriculum. The students web usage behavior supports to make enlightened decisions to improve the students performance and suggest recommendations for their academic perspectives (G.Vaitheeswaran and Arockiam, 2014).

Philip Sheridan Buffum (2014) examines three key aspects of a Big Data unit for middle school, its alignment with emerging curricular standards; the perspectives of middle school classroom teachers in mathematics, science, and language and student feedback as explored during a middle school pilot study with a small subset of the planned curriculum. The results indicate that a Big Data unit holds a greater promise in a middle school computer science curriculum.

Mohammed M. Abu Tair (2012) used Educational data mining technique and adopted to improve graduate students’ performance, and to overcome the problem of low grades of graduate students.

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