期刊論文
| 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 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/57429 ) |