Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

Tutut Herawan, Prima Vitasari and Zailani Abdullah
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Tutut Herawan: Universiti Malaysia Pahang, Malaysia
Prima Vitasari: Universiti Malaysia Pahang, Malaysia
Zailani Abdullah: Universiti Malaysia Terengganu, Malaysia

International Journal of Knowledge and Systems Science (IJKSS), 2012, vol. 3, issue 2, 24-41

Abstract: One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth (Significant Least Pattern Growth) for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang (UMP). The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

Date: 2012
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