Applying Hybrid Data Mining Techniques to Web-based Self-Assessment System of Study and Learning Strategies Inventory

doi:10.1016/j.eswa.2008.06.089

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/57429

Title:	Applying Hybrid Data Mining Techniques to Web-based Self-Assessment System of Study and Learning Strategies Inventory
Authors:	Shih, Chien-Chou;Chiang, Ding-An;Lai, Sheng-Wei;Hu, Yen-Wei
Contributors:	淡江大學資訊傳播學系;淡江大學資訊工程學系;淡江大學通識與核心課程中心
Keywords:	Data mining;Association rule;Decision tree;Self-assessment;LASSI
Date:	2009-04
Issue Date:	2011-09-14 14:40:49 (UTC+8)
Publisher:	Kidlington: Pergamon
Abstract:	Traditional assessment tools, such as “Learning and Study Strategy Scale Inventory (LASSI)”, are typically pen-and-paper tests that require responses to a multitude of questions. This may easily lead to student’s resistance, fatigue and unwillingness to complete the assessment. To improve the situation, a hybrid data mining technique was applied to analyze the LASSI surveys of freshmen students at Tamkang University. The most significant contribution of this research is in dynamically reducing the number of questions while the LASSI assessment is proceeding. To verify the appliance of the proposed method, a web-based LASSI self-assessment system (Web-LSA) was developed. This system can be used as a guide to determine study disturbances for high-risk groups, and can provide counselors with fundamental information on which to base follow-up counseling services to its users.
Relation:	Expert Systems with Applications 36(3)pt.1, pp.5523-5532
DOI:	10.1016/j.eswa.2008.06.089
Appears in Collections:	[Graduate Institute & Department of Information and Communication] Journal Article [Graduate Institute & Department of Computer Science and Information Engineering] Journal Article [center for general education and core curriculm ] Journal Article

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