Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets

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 suffer...

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Main Authors:	Tutut, Herawan, Haruna, Chiroma, Prima, Vitasari, Zailani, Abdullah, Maizatul Akmar, Ismail, Mohd Khalit, Othman
Format:	Article
Language:	English
Published:	Springer 2015
Subjects:	Q Science (General) QA75 Electronic computers. Computer science T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/1/Mining%20Critical%20Least%20Association%20Rules%20from%20Students%20Suffering%20Study%20Anxiety%20Darasets.pdf

id	ump-12361
recordtype	eprints
spelling	ump-123612016-03-22T07:42:05Z http://umpir.ump.edu.my/id/eprint/12361/ Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets Tutut, Herawan Haruna, Chiroma Prima, Vitasari Zailani, Abdullah Maizatul Akmar, Ismail Mohd Khalit, Othman Q Science (General) QA75 Electronic computers. Computer science T Technology (General) 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. Springer 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12361/1/Mining%20Critical%20Least%20Association%20Rules%20from%20Students%20Suffering%20Study%20Anxiety%20Darasets.pdf Tutut, Herawan and Haruna, Chiroma and Prima, Vitasari and Zailani, Abdullah and Maizatul Akmar, Ismail and Mohd Khalit, Othman (2015) Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets. Quality & Quantity, 49 (6). pp. 2527-2547. ISSN 0033-5177 http://link.springer.com/article/10.1007/s11135-014-0125-5 doi:10.1007/s11135-014-0125-5
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General) QA75 Electronic computers. Computer science T Technology (General)
spellingShingle	Q Science (General) QA75 Electronic computers. Computer science T Technology (General) Tutut, Herawan Haruna, Chiroma Prima, Vitasari Zailani, Abdullah Maizatul Akmar, Ismail Mohd Khalit, Othman Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
description	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.
format	Article
author	Tutut, Herawan Haruna, Chiroma Prima, Vitasari Zailani, Abdullah Maizatul Akmar, Ismail Mohd Khalit, Othman
author_facet	Tutut, Herawan Haruna, Chiroma Prima, Vitasari Zailani, Abdullah Maizatul Akmar, Ismail Mohd Khalit, Othman
author_sort	Tutut, Herawan
title	Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
title_short	Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
title_full	Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
title_fullStr	Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
title_full_unstemmed	Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets
title_sort	mining critical least association rules from students suffering study anxiety datasets
publisher	Springer
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/ http://umpir.ump.edu.my/id/eprint/12361/1/Mining%20Critical%20Least%20Association%20Rules%20from%20Students%20Suffering%20Study%20Anxiety%20Darasets.pdf
first_indexed	2023-09-18T22:13:57Z
last_indexed	2023-09-18T22:13:57Z
_version_	1777415211791155200

Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets

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