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

淡江大學機構典藏 > 文學院 > 資訊傳播學系暨研究所 > 期刊論文 > Item 987654321/57429

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题名:	Applying Hybrid Data Mining Techniques to Web-based Self-Assessment System of Study and Learning Strategies Inventory
作者:	Shih, Chien-Chou;Chiang, Ding-An;Lai, Sheng-Wei;Hu, Yen-Wei
贡献者:	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
关键词:	Data mining;Association rule;Decision tree;Self-assessment;LASSI
日期:	2009-04
上传时间:	2011-09-14 14:40:49 (UTC+8)
出版者:	Kidlington: Pergamon
摘要:	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.
關聯:	Expert Systems with Applications 36(3)pt.1, pp.5523-5532
DOI:	10.1016/j.eswa.2008.06.089
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