纵向非参数认知诊断评估

Nonparametric Methods for Longitudinal Cognitive Diagnosis

通过建立相邻时间点间被试知识状态和理想作答模式的链接,提出了4种逻辑简洁的纵向非参数认知诊断方法:LNPC、LWNPC、LGNPC和LWGNPC。模拟研究结果表明:建立的链接能提升纵向判准精度。与参数模型相比,4种方法估计精度相当,受样本容量影响小。与Long-HDD相比,4种方法判准精度较高,题目质量较低时LNPC和LWNPC仍有较好表现;实证研究表明:4种方法能够应用于实际纵向测验分析,与参数模型和Long-HDD判别一致性较高。推荐LWNPC方法。

This research proposed four concise longitudinal nonparametric diagnostic methods:longitudinal nonparametric classification(LNPC),longitudinal weighted nonparametric classification(LWNPC),longitudinal generalized nonparametric classification(LGNPC),and longitudinal weighted generalized nonparametric classification(LWGNPC),by leveraging the connection of students’knowledge state and the ideal response pattern between adjacent time points.The performance of the four new methods was evaluated by two simulation studies and an empirical study.The results of simulation studies showed that:(1)the established link could improve the accuracy of longitudinal classification;(2)compared to the HO-HMM model,the new methods could achieve similar precision while being less affected by the sample size;(3)the new methods outperformed the Long-HDD method in terms of accuracy,and the LNPC and LWNPC still performed well even with low-quality items.The results of the empirical study showed that the four new methods were highly consistent with the HO-HMM model and Long-HDD classification.We recommend using the LWNPC method to analyze the real data.