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    題名: 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
    貢獻者: 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
    關鍵詞: Data mining;Association rule;Decision tree;Self-assessment;LASSI
    日期: 2009-04
    上傳時間: 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
    顯示於類別:[資訊傳播學系暨研究所] 期刊論文
    [資訊工程學系暨研究所] 期刊論文
    [通識與核心課程中心] 期刊論文

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