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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2015
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848819221893480448 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T01:50:07Z |
| format | Article |
| id | ump-12361 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:50:07Z |
| publishDate | 2015 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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. (Published) http://link.springer.com/article/10.1007/s11135-014-0125-5 doi:10.1007/s11135-014-0125-5 |
| 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 |
| title | 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_short | Mining Critical Least Association Rules from Students Suffering Study Anxiety Datasets |
| title_sort | mining critical least association rules from students suffering study anxiety datasets |
| topic | Q Science (General) QA75 Electronic computers. Computer science T Technology (General) |
| 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 |