四种新的基于属性平衡的CD-CAT选题策略开发研究

基于属性平衡的CD-CAT选题策略能够保证每个认知属性被相当数量的题目测量，从而提高被试属性判准率，传统的基于属性平衡的选题策略包括MMGDI法和MGCDI法。本文针对传统的基于属性测量次数平衡选题策略进行改进，提出4种新的基于属性平衡的选题策略：RMGDI、RMCDI、SE-RMGDI、SE-RMCDI，前两种为基于属性测量次数平衡，后两种为基于属性测量精度平衡的选题策略。模拟研究表明：（1）定长CD-CAT条件下，短测验中，MMGDI表现最好，而长测验中，SE-RMGDI和SE-RMCDI的表现优于传统的属性平衡选题策略。（2）不定长CD-CAT条件下，RMGDI在判准率指标上表现优于传统的属性平衡选题策略，4种新的属性平衡策略在测量效率和综合指标上的表现均优于传统的选题策略。

Four New Item Selection Strategies Based on Attribute Balancing in CD-CAT

Cognitive diagnostic assessment can reveal examinee’s knowledge state which is very important for remedial teaching, and computerized adaptive testing (CAT) is a new mode of testing which is more efficient than the traditional paper and pencil testing. As a combination of cognitive diagnostic and computerized adaptive testing, the cognitive diagnostic computerized adaptive testing (CD-CAT) have gained more and more attentions by educational institutions. Item selection strategies is a fundamental component of CD-CAT, most item selection strategies didn’t consider about balanced coverage of the attributes. As a matter of fact, attribute balancing can make toward adequate coverage of every attribute and improve pattern correct classification rate. The literature review revealed that Cheng(2010) studied the item selection strategies based on attribute balancing, and proposed the MMGDI method. The other existing item selection strategies based on attribute balancing was the MGCDI method, which combines modified MMGDI with CDI. The MMGDI method and the MGCDI method can only ensure that each cognitive attribute was measured by roughly similar numbers of items. This paper proposed four new item selection strategies based on attribute balancing in CD-CAT. The new item selection strategies were revised maximum global discrimination index method (RMGDI), revised maximum cognitive diagnosis index method (RMCDI), RMGDI based on standard error of attribute (SE-RMGDI), RMCDI based on standard error of attribute (SE-RMGCDI), respectively. The RMGDI method and the RMCDI method can ensure that each cognitive attribute was measured by roughly similar numbers of items, yet the SE-RMGDI method and the SE-RMCDI method can ensure that each cognitive attribute was measured with roughly similar measurement accuracy. Two monte carlo simulation studies were conducted to compared new item selection strategies with the MMGDI method and the MGCDI method. The simulation results showed that: (1) Under the fixed-length CD-CAT, all the new item selection strategies based on attribute balancing were better at pattern correct classification rate than the traditional MGCDI method except when the testing length was 12, and only RMGDI, SE-RMGDI and SE-RMCDI method performed better than the MGCDI method when testing length was 12. Compare with the traditional MMGDI method, the SE-RMGDI method and the SE-RMCDI method performed better in pattern correct classification rate when the testing length was longer than 5, instead the MMGDI method was the best item selection strategy when the testing length was shorter than 5. (2) the PCCR of the RMGDI method was higher than the existing item selection strategies based on attribute balancing under variable-length CD-CAT, the test efficiency and the comprehensive performance of four new item selection strategies were better than existing item selection strategies based on balanced attribute coverage under variable-length CD-CAT. (3) In general, the performance of SE-RMGDI method and SE-RMCDI method was the best among all selection strategies based on attribute balancing, and the SE-RMCDI method was more recommended when the length of the fixed-length CD-CAT was short or the requested precision of the variable-length CD-CAT was lenient, instead the SE-RMGDI method had a better performance when the fixed-length CD-CAT was longer or the variable-length CD-CAT requested more stringent precision.