Cognitive Diagnostic Models With Attribute Hierarchies: Model Estimation With a Restricted Q-Matrix Design

Tu, Dongbo; Wang, Shiyu; Cai, Yan; Douglas, Jeffrey A.; Chang, Hua Hua

doi:10.1177/0146621618765721

Cited by 18 publications

(26 citation statements)

References 31 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also explains why the marginal CCRs of α 1 under Q 1 and Q 2 were substantially lower than those under Q 3 , while the marginal CCRs of the other two attributes did not differ much between Q-matrix designs. Including R T in the Q-matrix also increases the classification rates for the hierarchical cases in this study, which is consistent with the empirical findings from Tu et al (2019). The results for hierarchical attributes can also be explained from the identification perspective as discussed in Gu and Xu (forthcoming).…”

Section: Discussionsupporting

confidence: 90%

“…In contrast, some studies use all 2 K − 1 = 7 q-vectors in the Q-matrix as in an independent-attribute situation (Liu and Huggins-Manley, 2016;Liu et al, 2017), referred to as the unstructured Q-matrix approach. Tu et al (2019) took the restricted Q-matrix approach in a simulation study and emphasized the importance of containing the transpose of the R-matrix in the Q-matrix. Figure 1 provided an example Q-matrix containing the transpose of the R-matrix, R T .…”

Section: Introductionmentioning

confidence: 99%

“…This is especially true when the attributes are related to each other according to the cognitive theory. From a statistical perspective, the Q-matrix plays a significant role in model identification (Xu and Zhang, 2016 ; Xu, 2017 ; Köhn and Chiu, 2018 ; Gu and Xu, 2019a , forthcoming) and classification accuracy (DeCarlo, 2011 ; Madison and Bradshaw, 2015 ; Liu et al, 2017 ; Tu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…When attribute hierarchies are involved, there has not been a consensus on the Q-matrix design regarding whether all q-vectors are eligible (Templin and Bradshaw, 2014 ; Tu et al, 2019 ). When a test involves K independent attributes, there are 2 K − 1 distinct q-vectors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Q-Matrix Designs of Longitudinal Diagnostic Classification Models With Hierarchical Attributes for Formative Assessment

et al. 2020

View full text Add to dashboard Cite

Longitudinal diagnostic classification models (DCMs) with hierarchical attributes can characterize learning trajectories in terms of the transition between attribute profiles for formative assessment. A longitudinal DCM for hierarchical attributes was proposed by imposing model constraints on the transition DCM. To facilitate the applications of longitudinal DCMs, this paper explored the critical topic of the Q-matrix design with a simulation study. The results suggest that including the transpose of the R-matrix in the Q-matrix improved the classification accuracy. Moreover, 10-item tests measuring three linear attributes across three time points provided satisfactory classification accuracy for low-stakes assessment; lower classification rates were observed with independent or divergent attributes. Q-matrix design recommendations were provided for the short-test situation. Implications and future directions were discussed.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Q-Matrix Designs of Longitudinal Diagnostic Classification Models With Hierarchical Attributes for Formative Assessment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The Q-matrix in the cognitive diagnostic assessment we have constructed is a matrix used to connect test items and cognitive attributes, in which 1 represents the corresponding attribute that is considered in the test item, and 0 is the opposite. The Q-matrix has built a bridge between the observable responses of students and their unobservable cognitive states (Tu et al, 2019). According to the mark of the test item in the PISA 2012 manual, the Q-matrix is obtained, as shown in Table 2.…”

Section: Q-matrixmentioning

confidence: 99%

International Comparative Study on PISA Mathematics Achievement Test Based on Cognitive Diagnostic Models

Chang

et al. 2020

Front. Psychol.

Self Cite

View full text Add to dashboard Cite

As one of the most influential international large-scale educational assessments, the Program for International Student Assessment (PISA) provides a valuable platform for the horizontal comparisons and references of international education. The cognitive diagnostic model, a newly generated evaluation theory, can integrate measurement goals into the cognitive process model through cognitive analysis, which provides a better understanding of the mastery of students of fine-grained knowledge points. On the basis of the mathematical measurement framework of PISA 2012, 11 attributes have been formed from three dimensions in this study. Twelve test items with item responses from 24,512 students from 10 countries participated in answering were selected, and the analyses were divided into several steps. First, the relationships between the 11 attributes and the 12 test items were classified to form a Q matrix. Second, the cognitive model of the PISA mathematics test was established. The liner logistic model (LLM) with better model fit was selected as the parameter evaluation model through model comparisons. By analyzing the knowledge states of these countries and the prerequisite relations among the attributes, this study explored the different learning trajectories of students in the content field. The result showed that students from Australia, Canada, the United Kingdom, and Russia shared similar main learning trajectories, while Finland and Japan were consistent with their main learning trajectories. The primary learning trajectories of the United States and China were the same. Furthermore, the learning trajectory for Singapore was the most complicated, as it showed a diverse learning process, whereas the trajectory in the United States and Saudi Arabia was relatively simple. This study concluded the differences of the mastery of students of the 11 cognitive attributes from the three dimensions of content, process, and context across the 10 countries, which provided a reference for further understanding of the PISA test results in other countries and shed some evidence for a deeper understanding of the strengths and weaknesses of mathematics education in various countries.

show abstract

Diagnosing listening and reading skills in the Chinese EFL context: Performance stability and variability across modalities and performance levels

Xiong

Min

2022

System

View full text Add to dashboard Cite

Cognitive Diagnostic Models With Attribute Hierarchies: Model Estimation With a Restricted Q-Matrix Design

Cited by 18 publications

References 31 publications

Q-Matrix Designs of Longitudinal Diagnostic Classification Models With Hierarchical Attributes for Formative Assessment

Q-Matrix Designs of Longitudinal Diagnostic Classification Models With Hierarchical Attributes for Formative Assessment

International Comparative Study on PISA Mathematics Achievement Test Based on Cognitive Diagnostic Models

Diagnosing listening and reading skills in the Chinese EFL context: Performance stability and variability across modalities and performance levels

Contact Info

Product

Resources

About