Title: Mining critical least association rules of student suffering language and social anxieties

Authors: Tutut Herawan; Prima Vitasari; Zailani Abdullah

Addresses: Department of Mathematics Education, Universitas Ahmad Dahlan, Jalan Prof Dr Soepomo, Yogyakarta, 55166, Indonesia ' Faculty of Industrial Engineering, Institut Teknologi Nasional, Jl. Bendungan Sigura-gura No. 2, Malang, 65145, Indonesia ' Department of Computer Science, Faculty of Science and Technology, University Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia

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.

Keywords: data mining; least association rules; critical least support; language anxiety; social anxiety; association rules mining; significant least pattern growth; engineering students; engineering education; higher education; Malaysia.

DOI: 10.1504/IJCEELL.2013.054288

International Journal of Continuing Engineering Education and Life-Long Learning, 2013 Vol.23 No.2, pp.128 - 146

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 02 Jun 2013 *

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