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Educational Data Mining Review: Teaching Enhancement

Educational Data Mining Review: Teaching Enhancement

Rashmi Agrawal, Neha Gupta
Copyright: © 2017 |Pages: 17
ISBN13: 9781522524861|ISBN10: 152252486X|EISBN13: 9781522524878
DOI: 10.4018/978-1-5225-2486-1.ch007
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MLA

Agrawal, Rashmi, and Neha Gupta. "Educational Data Mining Review: Teaching Enhancement." Privacy and Security Policies in Big Data, edited by Sharvari Tamane, et al., IGI Global, 2017, pp. 149-165. https://doi.org/10.4018/978-1-5225-2486-1.ch007

APA

Agrawal, R. & Gupta, N. (2017). Educational Data Mining Review: Teaching Enhancement. In S. Tamane, V. Solanki, & N. Dey (Eds.), Privacy and Security Policies in Big Data (pp. 149-165). IGI Global. https://doi.org/10.4018/978-1-5225-2486-1.ch007

Chicago

Agrawal, Rashmi, and Neha Gupta. "Educational Data Mining Review: Teaching Enhancement." In Privacy and Security Policies in Big Data, edited by Sharvari Tamane, Vijender Kumar Solanki, and Nilanjan Dey, 149-165. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2486-1.ch007

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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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