The Application of RaschGSP IRT Theory for Large Data Sets in Educational Measurement

• Main Authors: Nguyen Phung Tuyen, 阮逢選
• Other Authors: Sheu Tian-Wei
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/64505721262607513253

博士 === 國立臺中教育大學 === 教育資訊與測驗統計研究所 === 103 === In the trend of student numbers becoming increasingly fewer in a class, a good educational assessment method not only satisfies the important criteria such as valid, reliable, and feasible but also has to be flexible in assessment; it can well handle the small samples as well as the large samples to create the unity in all cases. The purpose of this paper is to propose the combination of Grey System Theory, RaschGSP IRT Theory, and Receiver Operating Characteristic (ROC) analysis to build up new assessment methods to meet context of student numbers increasingly fewer in a class. This dissertation focuses on ways to assess the effectiveness of the proposed methods in handling both small and large samples of educational tests. The research approach adopted in this dissertation includes theoretical study and experimental study to aim at the comparison of the results processed by proposed methods and by the previous models for validity and reliability. The findings from this study are shown as follows: (1) The new assessment methods have been proposed; The proposed methods consists of the method to evaluate difficulty of questions and ability of students, method to evaluate quality of test and its suitability, method to evaluate ability level of class, and method to establish standard of test. All of them not only well handle small samples but also can apply for large samples. (2) The proposed methods agreed with the previous models; The experimental results were compared with results processed by the previous models. The comparison results showed that the suitability between the models was high. (3) The proposed methods had the potential for diverse assessment; The proposed methods are considered for treatment of large samples due to similarity of their assessment results with the results processed by previous models. On the other hand, because of the ability for better handling small samples, so they have prospects of being applied in the context of student numbers increasingly fewer in a class. Theoretical contributions and general implications of the findings are discussed.