Cognitive diagnostic assessment (CDA) is able to obtain information regarding the student's cognitive and knowledge development based on the psychometric model. Notably, most of previous studies use traditional cognitive diagnosis models (CDMs). This study aims to compare the traditional CDM and the longitudinal CDM, namely, the hidden Markov model (HMM)/artificial neural network (ANN) model. In this model, the ANN was applied as the measurement model of the HMM to realize the longitudinal tracking of students' cognitive skills. This study also incorporates simulation as well as empirical studies. The results illustrate that the HMM/ANN model obtains high classification accuracy and a correct conversion rate when the number of attributes is small. The combination of ANN and HMM assists in effectively tracking the development of students' cognitive skills in real educational situations. Moreover, the classification accuracy of the HMM/ANN model is affected by the quality of items, the number of items as well as by the number of attributes examined, but not by the sample size. The classification result and the correct transition probability of the HMM/ANN model were improved by increasing the item quality and the number of items along with decreasing the number of attributes.

中文翻译：

---

基于HMM / ANN模型的纵向认知诊断评估。

认知诊断评估（CDA）能够基于心理测量模型获得有关学生的认知和知识发展的信息。值得注意的是，以前的大多数研究都使用传统的认知诊断模型（CDM）。本研究旨在比较传统的CDM和纵向CDM，即隐马尔可夫模型（HMM）/人工神经网络（ANN）模型。在该模型中，将人工神经网络作为HMM的度量模型，以实现对学生认知能力的纵向跟踪。该研究还结合了模拟和实证研究。结果表明，当属性数量较少时，HMM / ANN模型具有较高的分类精度和正确的转换率。ANN和HMM的结合有助于在真实的教育环境中有效地跟踪学生认知能力的发展。此外，HMM / ANN模型的分类准确性受项目质量，项目数量以及所检查的属性数量的影响，但不受样本大小的影响。HMM / ANN模型的分类结果和正确的转移概率通过提高项目质量和项目数量以及减少属性数量而得到改善。