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

Authors:
Chien-Chou Shih;Ding-An Chiang;Sheng-Wei Lai;Yen-Wei Hu
Affiliations:
Department of Information and Communication, Tamkang University, Taiwan, ROC;Department of Computer Science and Information Engineering, Tamkang University, Rm. E690, Taiwan, ROC;Department of Computer Science and Information Engineering, Tamkang University, Rm. E690, Taiwan, ROC;Center for General Education and Core Curriculum, Tamkang University, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.