Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education

Abstract

In the past decade, we have witnessed a tremendous rise in the use of electronic devices in education. Starting from nursery classes at the preschool level to the postgraduate programs at the universities, electronic devices are being used extensively to enhance and facilitate quality of education. Although the use of computer networks is an inherent feature of online learning, the traditional schools and universities are also making extensive use of network-connected electronic devices such as mobile phones, tablets, and computers. However, it is humanly impossible to analyze enormous volume of data generated from the active usage of devices connected through a large network. The educators and academic administrators can benefit from their counterparts in business and service industries where a complex system of methods and techniques, usually referred to as data analytics or data mining, is being used to analyze a large influx of real-time data in decision-making. Researchers have started paying attention to the application of data mining and data analytics to handle big data generated in the educational sector. In the context of education, these techniques are specifically referred to as educational data mining (EDM) and learning analytics (LA). Generally, EDM looks for new patterns in data and develops new algorithms and/or new models, while LA applies known predictive models in instructional systems. This chapter starts by describing major EDM and LA techniques used in handling big data in commercial and other activities. It will also provide a brief description of how EDM and LA are affecting the typical stakeholders of a higher education institution. Furthermore, the chapter will provide a detailed account of how these techniques are used to analyze the learning process of students, assessing their performance and providing them with detailed feedback in real time. These Technologies can also assist in planning administrative strategies to provide quality services to all stakeholders of an educational institution. Not all the stakeholders involved in providing education are experts of big data. However, in order to meet their analytical requirements, the researchers have developed easy-to-use data mining and visualization tools. In this chapter, we have provided the necessary details of some of these tools. The institutions/governments across the world are adopting EDM/LA to frame strategic policies, to understand the students’ learning behaviors, etc. As case studies, we have also discussed some implementation of EDM and LA techniques in universities in different countries.