Applying Hybrid Data Mining Techniques to Web-based Self-Assessment System of Study and Learning Strategies Inventory
學年 97
學期 2
出版（發表）日期 2009-04-01
作品名稱 Applying Hybrid Data Mining Techniques to Web-based Self-Assessment System of Study and Learning Strategies Inventory
作品名稱（其他語言）
著者 Shih, Chien-Chou; Chiang, Ding-An; Lai, Sheng-Wei; Hu, Yen-Wei
單位 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
出版者 Kidlington: Pergamon
著錄名稱、卷期、頁數 Expert Systems with Applications 36(3)pt.1, pp.5523-5532
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.
關鍵字 Data mining;Association rule;Decision tree;Self-assessment;LASSI
語言 en
ISSN 0957-4174
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Lai, Sheng-Wei
審稿制度 是
國別 GBR
公開徵稿
出版型式 紙本
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