心理科学 ›› 2024, Vol. 47 ›› Issue (4): 947-958.DOI: 10.16719/j.cnki.1671-6981.20240423

• 统计、测量与方法 • 上一篇    下一篇

融入能力信息的认知诊断模型开发与应用*

宋丽红1, 胡海洋2, 汪文义**2, 丁树良2, 袁思玉2   

  1. 1江西师范大学教育学院,南昌,330022;
    2江西师范大学计算机信息工程学院,南昌,330022
  • 出版日期:2024-07-20 发布日期:2024-07-17
  • 通讯作者: ** 汪文义,E-mail: wenyiwang@jxnu.edu.cn
  • 基金资助:
    *本研究得到国家自然科学基金 (62267004,62067005,61967009)和江西省高等学校教学改革研究课题(JXJG-22-2-44)的资助

Development of a Cognitive Diagnostic Model with Ability Covariate and Its Applications

Song Lihong1, Hu Haiyang2, Wang Wenyi2, Ding Shuliang2, Yuan Siyu2   

  1. 1School of Education, Jiangxi Normal University, Nanchang, 330022;
    2School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022
  • Online:2024-07-20 Published:2024-07-17

摘要: 新时代教育评价改革对评价的科学性、客观性和专业性提出了更高的要求。以一般(高阶)能力作为连接项目反应理论模型与认知诊断模型的桥梁,相互利用不同试题上的作答信息,开发出融入能力信息的认知诊断模型。模拟研究考查了新模型在四种参数分布和五种题量下的表现,并与DINA模型与2PLM进行了比较。结果显示:在相同题量下,新模型能力参数的均方误差优于2PLM的均方误差;新模型的模式判准率优于DINA模型的模式判准率;属性斜率越大,新模型的属性判准率和能力精度更高。在英语水平证书考试的实测数据上,新模型相对拟合指标优于DINA和HO-DINA等模型;新模型虽稍逊于2PLM,但两者所得能力与测验总分相关较高;新模型在属性2上分类准确性高于其他模型,分析发现新模型可利用能力信息提高Q阵中考查次数较少属性的分类准确性。

关键词: 能力, 知识状态, 2PL模型, DINA模型, MCMC算法

Abstract: The item response theory (IRT) is an important model to estimate the ability parameters, and the cognitive diagnosis model (CDM) is a vital model to diagnose the cognitive structure of the examinees. Because test item responses contain the information about the ability parameters and the knowledge states and their relationship, how to fully utilize the information to further enhance the accuracy of the knowledge states, becomes very important for helping to decrease the number of tests and test lengths.
Considering the strong relationship between higher-order DINA (HO-DINA) model and two-parameter logistic model (2PLM), this new statistical measurement model was proposed by regarding the general ability as higher-order ability and estimated by Markov Chain Monte Carlo. In the estimation of ability, the knowledge states are considered as response patterns in establishing the relationship between the ability and knowledge states. Five attributes were considered in the study. Test consists of two parts of items, one is fitted by 2PLM and the other is fitted by the DINA model. The simulation study examined the performance of the new model with four model parameter distributions and five different test lengths, and compared it with the DINA model and 2PLM respectively. In the analysis of Examination for the Certificate of Proficiency in English (ECPE) test data, the absolute fit indices, -2LL, AIC, BIC and DIC were provided as an example to evaluate the model-data fit.
The results show that: (a) The new proposed model in this study can obtain the ability parameters and cognitive structure parameters of the examinees in one test, and it has a good recovery for the estimation of attribute patterns, item parameters, and ability parameters. In particular, the accuracy of ability parameters has been greatly improved, and the MCMC algorithm is feasible; (b) The longer the CDM-based test length is, the higher the correct classification rate of knowledge state is, and the same is true for the ability estimation based on 2PLM; (c) Compared with lognormal distribution, the error of ability estimation is slightly smaller when the item discriminations followed from uniform distribution. When the slip parameters and the guessing parameters are small, the correct classification rate for attributes or attribute patterns are higher. In addition, the analysis of the ECPE data shows that the estimation accuracy of the new model is reasonable and has practical implications.
Because there is always a relationship between the levels of ability and the mastery of knowledge, the ability of estimation enriched in test items fitting by an item response theory model can provide information for the classification of knowledge state, and item only fitting the CDM can also indirectly provide the information to improve the precision of ability estimation. In addition, it does not need calibrate the attribute vector for all test items and not require all items fitted two models at the same time. The new model uses a general or high-order ability as bridge between item response theory and cognitive diagnostic models, utilizing information from different test items to improve the accuracy of abilities and knowledge, tates estimation.

Key words: ability, knowledge state, 2PLM, the DINA Model, MCMC