多级评分认知诊断模型述评

Research Progress in Polytomous Cognitive Diagnosis Model

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生认知优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1计分的认知诊断模型,但对于多级计分认知诊断模型的研究还比较少。本文对已有的多级计分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级计分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级计分诊断模型的研究方向进行了展望。

Educational assessments is playing an increasingly important role in assessing students’ academic achievements. Traditional test theory can only provide students with a total score or ability value, and while cannot provide specific information about students’ internal knowledge structure and learning process. Cognitively diagnostic assessment(CDA) aims to measure learners’ cognitive strengths and weaknesses in assessed skills, so as to provide immediate diagnostic information for parents and schools, and guide subsequent improvement of teaching strategies and objectives. Currently, a large number of cognitive diagnosis models(CDMs) have been proposed. However, most existing CDMs are only suitable for dichotomously scored items. In the case of dichotomously scored items, the test manager classifies the observed responses into two categories, namely correct and incorrect. In practice, there are larger polytomously scored items/data in educational and psychological tests. It is common to use likert-type items in psychological and social science questionnaires. For example, an item has four response categories of "strongly dislike", "dislike", "uncertainty", and "strongly like", usually scores 0, 1, 2, and 3, respectively. In educational achievement test, it is also common to have polytomous items where a higher response category indicates higher ability to measure. It has been recognized that polytomous items have several advantages over dichotomous items. For example, polytomous items can provide more diagnostic information for inference, and some features are easier to measure with polytomous items such as personality, attitude, motivation, interest and more. Therefore, it is very necessary to develop CDM for polytomous data. At present, only a few polytomous CDMs have been developed to deal with polytomous items. According to the models’ different order-preserving mechanisms in forming the dichotomies of response categories, the existing polytomous CDMs can be divided into three types:(1) Partial credit models that make use of the local(or adjacent category) category logit,(2) Sequential models, based on the continuation ratio logit, and(3) Graded response models, based on global(or cumulative) logit. This paper briefly introduces the most commonly used polytomous CDM, including their parameterization, the meaning of the model parameters, the model assumptions, the applicable scope of the model and relationship between these models, so as to provide a model reference for researchers and practical users. To explore the potential of these proposed polytomous CDMs, several future research directions can be identified. First, this paper only introduces parameterized polytomous CDM. Future studies can also compare the advantages and disadvantages of parameterized and nonparameterized polytomous CDMs in practical applications, so as to provide suggestions for actual users in choosing these two types of models. Second, most CDMs assume that all students use the same strategy to solve problems. Multi-strategy CDMs take into account the differences of problem solving strategies among students and help to provide more diagnostic information. Therefore, it will be an interesting direction to carry out the polytomous CDM for multiple strategies. Third, current CDM parameter estimation software may be difficult for real users with no basis of programming. If we can develop a CDM data analysis software or platform with simpler operation and visualization, it will be helpful to promote the practical application of cognitive diagnosis.