Bayesian Estimation of the DINA Q matrix

Chen, Yinghan; Culpepper, Steven Andrew; Chen, Yuguo; Douglas, Jeffrey A.

doi:10.1007/s11336-017-9579-4

Cited by 101 publications

(92 citation statements)

References 32 publications

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“…It was found that attributes defined in the Q matrix did not contain the information about the degree of complexity of the objects. In the future, we will further validate this Q matrix using many recent techniques in psychometrics (e.g., Chen et al, 2018a). Bolshakova, N., and Azuaje, F. (2003).…”

Section: Discussionmentioning

confidence: 99%

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Wang

et al. 2020

Front. Psychol.

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This study reported on development and evaluation of a learning program that integrated a multidimensional diagnostic assessment with two different learning interventions with the aim to diagnose and improve three-dimensional mental rotation skills. The multidimensional assessment was built upon the Diagnostic Classification Model (DCM) framework that can report the binary mastery on each specific rotation skill. The two learning interventions were designed to train students to use a holistic rotation strategy and a combined analytic and holistic strategy, respectively. The program was evaluated through an experiment paired with multiple exploratory and confirmatory statistical analysis. Particularly, the recently proposed joint models for response times and response accuracy within dynamic DCM framework is applied to assess the effectiveness of the learning interventions. Compared with the traditional assessment on spatial skills, where the tests are timed and number correct is reported as a measure for test-takers' performances, the developed dynamic diagnostic assessment can provide an informative estimate of the learning trajectory for each participant in terms of the strengths and weaknesses in four fine-grained spatial rotation skills over time. Compared with an earlier study that provided initial evidence of the effectiveness of building a multidimensional diagnostic assessment with training tools, the present study improved the assessment and learning intervention design. Using both response times and response accuracy, thus current study additionally evaluated the newly developed program by investigating the effectiveness of two interventions across gender, country and rotation strategy.

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Section: Discussionmentioning

confidence: 99%

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Wang

et al. 2020

Front. Psychol.

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“…For the estimation of the RSDM, we used priors of N(0, 20) for each item parameter and Dirichlet(2) for each attribute profile, similar to Liu and Jiang (2018). These priors are considered less informative and have been recommended in similar DCM studies, such as Chen, Culpepper, Chen, and Douglas (2018) and Jiang and Carter (2018). For the model constraints, we successfully implemented the first constraint as described in the model.…”

Section: Discussionmentioning

confidence: 99%

A general diagnostic classification model for rating scales

Liu

Jiang

2019

Behav Res

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This study proposes and evaluates a general diagnostic classification model (DCM) for rating scales. We applied the proposed model to a dataset to compare its performance with traditional DCMs for polytomous items. We also conducted a simulation study based on the applied study condition in order to evaluate the parameter recovery of the proposed model. The findings suggest that the proposed model shows promise for (1) accommodating much smaller sample sizes by reducing a large number of parameters for estimation; (2) obtaining item category response probabilities and individual scores very similar to those from a traditional saturated model; and (3) providing general item information that is not available in traditional DCMs for polytomous items.

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“…iii The author do, however, note that this Q-matrix has been suggested to be revised by some previous studies (e.g., Chen, Culpepper, Chen & Douglas, 2018). But it is still fair to compare several models with the same Q-matrix, especially when there is no definite conclusion about the revision of this Q-matrix.…”

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confidence: 90%

Deterministic Inputs, Noisy Mixed Modeling for Identifying Coexisting Condensation Rules

Zhan¹

2020

Preprint

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In cognitive diagnosis models, the condensation rule reflects how latent attributes influence individuals’ observed item responses. Multiple condensation rules may be involved in an item simultaneously. The purpose of this study is to present a new cognitive diagnosis model that considers such coexisting condensation rules while keeping the model as simple as possible. Inspired by but different from the mixture/hybrid item response models, the deterministic-inputs, noisy mixture (DINMix) model is proposed. Specifically, the DINMix model focuses on the mixture of condensation rules employed in the latent responses instead of the mixture of latent groups in the population. Two simulation studies were conducted to evaluate the psychometric properties of the proposed model. The results indicate that the model parameters for the DINMix model can be well recovered, and the DINMix model can adaptively and accurately identify coexisting condensation rules. Two empirical examples are also analyzed to illustrate the applicability and advantages of the proposed model. The results indicate that in many, if not all, test applications, no single condensation rule can be expected to be contained in all the items.

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Bayesian Estimation of the DINA Q matrix

Cited by 101 publications

References 32 publications

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

A general diagnostic classification model for rating scales

Deterministic Inputs, Noisy Mixed Modeling for Identifying Coexisting Condensation Rules

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