Mining critical least association rules of student suffering language and social anxieties Online publication date: Mon, 30-Dec-2013
by Tutut Herawan; Prima Vitasari; Zailani Abdullah
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 23, No. 2, 2013
Abstract: One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth (SLP-growth) proposed by Abdullah et al. (2010a) for capturing interesting rules in student suffering language and social anxieties dataset. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It can be helpful to assist students in handling their fear of language and social. Furthermore, it is also useful in increasing the quality of learning.
Online publication date: Mon, 30-Dec-2013
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