Big Data in Higher Education

Big Data in Higher Education

Marta Vidal (Complutense University of Madrid, Spain), Javier Vidal-García (University of Valladolid, Spain) and Rafael Hernández Barros (Complutense University of Madrid, Spain)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/978-1-5225-7501-6.ch068
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Big Data refers to large volumes of information – on diseases, ticket sales, and so on – that standard database tools such as MySQL and Oracle, cannot easily process. Thus, data analytics tools, such as InfoGram and Google Fusion Tables, are required to manage the information. The processed data is useful in several ways. For instance, public health officials may use the results of the analysis to explain the spread of viruses including the H1N1 virus (Mayer-Schönberger & Cukier, 2014, p. 2). Airplane companies may use the results to predict changes in ticket prices. Apart from the medical and aviation industries, institutions of higher learning also collect significantly large quantities of data. Hence, the analysis of Big Data also takes place in higher education. The beneficiaries of the analysis include students and administrators.
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The educational environment in the institutions of higher learning has considerably improved. Technological advancements are mainly responsible for the positive changes. Specifically, the introduction of data analytics in higher education – universities and colleges –made it easier for the management to make progress in key areas such as finance, student evaluation, and resource allocation (Bichsel, 2012). In other words, Big Data analytics aids college and university administrators to make predictions with respects to campus operations.

Before delving further into the uses of Big Data analytics in higher education, it is necessary to explain a few concepts about Big Data. Prior to the introduction of Big Data analytics, regular computers collected, contained, processed, and managed raw information. However, soon the data became so voluminous that the traditional computers could not process it efficiently. This prompted engineers to overhaul the existing data analytics tools (Mayer-Schönberger & Cukier, 2014, p. 6). As a result, processing technologies such as MapReduce and Google’s Hadoop were developed. These technologies manage terabytes of data by arranging the information in structured database tables. Thus, the database tables provide the raw materials for data analytics.

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