Educational Data Mining Review: Teaching Enhancement

Educational Data Mining Review: Teaching Enhancement

Rashmi Agrawal (Manav Rachna International Institute of Research and Studies, India) and Neha Gupta (Manav Rachna International Institute of Research and Studies, India)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/978-1-5225-2486-1.ch007

Abstract

In today's era, educational data mining is a discipline of high importance for teaching enhancement. EDM techniques can reveal useful information to educators to help them design or modify the structure of courses. EDM techniques majorly include machine learning and data mining techniques. In this chapter of the book, we will deliberate upon various data mining techniques that will help in identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, optimizing subject curriculum renewal. Various applications of data mining are also discussed by quoting example of various case studies. Analysis of social networks in educational field to understand student network formation in classrooms and the types of impact these networks have on student is also discussed.
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Cyclic Application Of Data Mining In Educational Systems

Data mining techniques are useful in discovering useful information that can assist educators in formative evaluation of educational systems when designing or modifying an environment or teaching approach. Below is the cyclic graph showing the application of data mining in any educational system.

Figure 1.

Cyclic application of data mining in educational systems

978-1-5225-2486-1.ch007.f01

In Figure 1, educators are responsible for designing, planning, building and maintaining of educational systems. The educational systems may belong to traditional classrooms, web based systems, adaptive systems or e-learning systems. These educational systems provide academic and interactive data about students, their usage patterns, course information etc. Various data mining techniques like clustering, classification, text mining and pattern recognition are applied on these systems to mine the useful information that helps in improving the teaching-learning process. This discovered knowledge is useful for both educators and students. The mined knowledge helps educators in better designing and maintenance of educational systems and helps the educators to evaluate the instructional design in a formative manner.

This mined knowledge helps the educators, students and the educational systems to guide facilitate and enhance learning as a whole.

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