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2020 | OriginalPaper | Chapter

13. Association Rule Mining in Higher Education: A Case Study of Computer Science Students

Authors : Njoud Alangari, Raad Alturki

Published in: Smart Infrastructure and Applications

Publisher: Springer International Publishing

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Abstract

Data mining (DM) is gaining significant importance these days because of the flow and accumulation of data from various sources and fields; it has been estimated that the world’s databases double every 20 months (Tan et al. Introduction to data mining, Pearson Addison Wesley, Boston, 2005. The data are growing not only in terms of their volume but also in terms of their complexity and diversity. Thus, DM techniques and tools are required, and many algorithms and techniques have been introduced. These techniques include clustering, association, and prediction via classification and regression. These techniques are used in many fields such as business, healthcare, and education. In this chapter, we explore the application of some of these techniques in education. In the educational field, instructors tend to use their experience and personal judgment to link grades and failures of students between courses on the basis of their knowledge of the courses’ content. As a result, major plans may be changed, and academic guidance is offered accordingly. However, such an opinion is not always validated and tested, and we cannot be certain of it. With the existence of DM techniques, along with the vast volumes of data held by education systems, universities and schools can predict students’ performance and find associations between many attributes such as course grades. The results of using these techniques can have a profound effect in helping to change program plans and offer guidance. Students can make better-informed decisions when presented with facts that can have effects on their study. In this chapter, we review DM techniques used to mine student data with a focus on association rule mining. We report our work on association rule mining of Computer Science students’ grades and address some related issues. In this work we used lift, Kulczynski (Kulc), and the imbalance ratio (IR) to measures the interestingness of the rules. Our results showed cases of correlation between courses with confidence from 80 to 100%.

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Metadata
Title
Association Rule Mining in Higher Education: A Case Study of Computer Science Students
Authors
Njoud Alangari
Raad Alturki
Copyright Year
2020
Book
Smart Infrastructure and Applications

Print ISBN: 978-3-030-13704-5

Electronic ISBN: 978-3-030-13705-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-13705-2_13