Mining critical least association rules of student suffering language and social anxieties. (1st January 2013)

• Record Type: Journal Article
• Authors: Herawan, Tutut; Vitasari, Prima; Abdullah, Zailani
• Is Part Of: International journal of continuing engineering education and lifelong learning. Volume 23:Number 2(2013)
• Page Start: 128
• Page End: 146
• Publication Date: 2013-01-01
• Subjects: data mining -- least association rules -- efficient algorithm -- critical least support -- language and social anxiety; Engineering -- Study and teaching -- Periodicals; 620.00715
• Languages: English
• ISSNs: 1560-4624

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.