Mining critical least association rules from students suffering study anxiety datasets

作     者：Herawan, Tutut Chiroma, Haruna Vitasari, Prima Abdullah, Zailani Ismail, Maizatul Akmar Othman, Mohd Khalit 

作者机构：Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Inst Teknol Nas Postgrad Program Jawa Barat Indonesia Univ Malaysia Terengganu Dept Comp Sci Kuala Terengganu 21030 Malaysia 

出 版 物：《QUALITY & QUANTITY》 (质与量)

年 卷 期：2015年第49卷第6期

页      面：2527-2547页

核心收录：

学科分类：0303[法学-社会学] 03[法学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：University of Malaya through UMRG [RP002F-13ICT] 

主　　题：Evaluation methodologies Simulations Programming and programming languages Computer-mediated communication 

摘      要：In data mining, association rules mining is one of the common and popular techniques used in various domain applications. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth for capturing interesting rules from students suffering from exam, family, presentation and library anxiety datasets. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang. The results of this research will provide useful information for educators to make more accurate decisions concerning their students, and to adapt their teaching strategies accordingly. Moreover, it can also highlight the role of non-academic staff in supporting learning environments for students. The obtained findings can be very helpful in assisting students to handle their fear and anxiety, and, finally, increasing the quality of the learning processes at the university.