Applying hybrid data mining techniques to web-based self-assessment system of Study and Learning Strategies Inventory

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.