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首页> 外文期刊>Expert systems with applications >Applying Hybrid Data Mining Techniques To Web-based Self-assessment System Of Study And Learning Strategies Inventory
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Applying Hybrid Data Mining Techniques To Web-based Self-assessment System Of Study And Learning Strategies Inventory

机译：将混合数据挖掘技术应用于基于Web的学习策略库存自我评估系统

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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.
机译：传统的评估工具，例如“学习和研究策略量表（LASSI）”，通常是笔试，需要回答多个问题。这很容易导致学生的抵抗，疲劳和不愿意完成评估。为了改善这种情况，使用了一种混合数据挖掘技术来分析淡江大学新生的LASSI调查。这项研究的最重要贡献是在进行LASSI评估时动态减少了问题数量。为了验证所提出方法的实用性，开发了基于Web的LASSI自评估系统（Web-LSA）。该系统可以用作确定高危人群学习障碍的指南，并且可以为辅导员提供基本信息，以此为基础向使用者提供后续辅导服务。

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