Mining critical least association rules from students suffering study anxiety datasets

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 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. Quality & Quantity Springer Journals

Mining critical least association rules from students suffering study anxiety datasets

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Springer Netherlands
Copyright © 2014 by Springer Science+Business Media Dordrecht
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
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