提高分类准确率的诊断性测验的研究

Study on Classification Accuracy Improvement in Diagnostic Test

传统的纸笔测验只给被试提供一个测验分数,计算机化自适应测验(CAT)不仅给出了被试的测验分数,还估计了被试的能力。但这两种方法都没有对学生的认知结构进行探索,认知诊断评价(CDA)的优势就在于能够揭示个人在所测知识领域的详细信息,对个体进行补救,教师也可以针对学生的知识状态改进教学,对教、学双方都有指导作用。传统的测验构建方法没有考虑可达矩阵,导致分类准确率不高,本文提出将可达矩阵与认知诊断信息指标相结合的选题策略,模拟实验结果表明,模式判准率和平均边际判准率都有所提高。

Traditional test only provides a total score,and computer adaptive test（CAT） not only provides examinee＇s score but also estimates examinee＇s ability.However,both two methods have not studied students＇ cognitive states.The advantage of cognitive diagnostic assessment（CDA） is that it could expose examinee＇s specific information and give effective remedy.Teachers can also improve teaching according to students＇ knowledge states.So CDA plays an important role in guiding teaching and studying.Traditional test does not include the reachability matrix;therefore the classification accuracy is low.To improve the classification accuracy,a new selection strategy,which combines the reachability matrix with cognitive diagnostic information index is developed.Monte Carlo simulation shows that the pattern classification rate and mean marginal classification rate are improved.