Exploring Multiple Strategic Problem Solving Behaviors in Educational Psychology Research by Using Mixture Cognitive Diagnosis Model

Frontiers in Psychology

Jing Yang¹  Shanshan Sun²  Jiwei Zhang³  Jing Lu⁴  Zhaoyuan Zhang⁵ 

[1] College of Mathematics, Taiyuan University of Technology, Jinzhong, China;Government of Jilin Province, Changchun, China;Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, Kunming, China;Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China;School of Mathematics and Statistics, Yili Normal University, Yili, China;

关键词: Bayesian inference;    cognitive diagnosis;    classification;    Markov chain Monte Carlo;    multiple-strategy models;   

DOI  :  10.3389/fpsyg.2021.568348

来源: Frontiers

【 摘 要 】

A mixture cognitive diagnosis model (CDM), which is called mixture multiple strategy-Deterministic, Inputs, Noisy “and” Gate (MMS-DINA) model, is proposed to investigate individual differences in the selection of response categories in multiple-strategy items. The MMS-DINA model system is an effective psychometric and statistical approach consisting of multiple strategies for practical skills diagnostic testing, which not only allows for multiple strategies of problem solving, but also allows for different strategies to be associated with different levels of difficulty. A Markov chain Monte Carlo (MCMC) algorithm for parameter estimation is given to estimate model, and four simulation studies are presented to evaluate the performance of the MCMC algorithm. Based on the available MCMC outputs, two Bayesian model selection criteria are computed for guiding the choice of the single strategy DINA model and multiple strategy DINA models. An analysis of fraction subtraction data is provided as an illustration example.

【 授权许可】

CC BY