A Class of Cognitive Diagnosis Models for Polytomous Data

Gao, Xuliang; Ma, Wenchao; Wang, Daxun; Cai, Yan; Tu, Dongbo

doi:10.3102/1076998620951986

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…CDM for Classroom-Level Assessments where p is the number of model parameters. In the CDM context, the AIC, BIC, and CAIC indices have been shown to adequately perform at selecting the generating model under the presence of response process or Q-matrix misspecifications (Chen et al, 2013;Gao et al, 2021). The posterior probability of examinee i belonging to latent class l can be derived from the likelihood:…”

Section: Relative Fit and Estimated Classification Accuracymentioning

confidence: 99%

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The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments

Nájera¹,

Abad

Chiu

et al. 2023

Journal of Educational and Behavioral Statistics

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The nonparametric classification (NPC) method has been proven to be a suitable procedure for cognitive diagnostic assessments at a classroom level. However, its nonparametric nature impedes the obtention of a model likelihood, hindering the exploration of crucial psychometric aspects, such as model fit or reliability. Reporting the reliability and validity of scores is imperative in any applied context. The present study proposes the restricted deterministic input, noisy “and” gate (R-DINA) model, a parametric cognitive diagnosis model based on the NPC method that provides the same attribute profile classifications as the nonparametric method while allowing to derive a model likelihood and, subsequently, to compute fit and reliability indices. The suitability of the new proposal is examined by means of an exhaustive simulation study and a real data illustration. The results show that the R-DINA model properly recovers the posterior probabilities of attribute mastery, thus becoming a suitable alternative for comprehensive small-scale diagnostic assessments.

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Section: Relative Fit and Estimated Classification Accuracymentioning

confidence: 99%

“…where p is the number of model parameters. In the CDM context, the AIC, BIC, and CAIC indices have been shown to adequately perform at selecting the generating model under the presence of response process or Q-matrix misspecifications (Chen et al, 2013; Gao et al, 2021).…”

Section: Relative Fit and Estimated Classification Accuracymentioning

confidence: 99%

The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments

Nájera¹,

Abad

Chiu

et al. 2023

Journal of Educational and Behavioral Statistics

View full text Add to dashboard Cite

show abstract

“…In addition, compared with CAIC and BIC, BIC has better performance in selecting the most suitable model. By analyzing the data, the application of CDMS is proved [6]. e learning management system of online teacher community is an online resource developed, which supports teachers in different fields to learn the advantages of their resources and contain realistic case experiences.…”

Section: Quotationmentioning

confidence: 99%

“…where λ and α are constant parameters, which are used to fit Ebbinghaus forgetting curve, and β parameter is the situation that students answer questions t j in history; if students answer correctly, it is 1; otherwise, the value is 0. count (B) indicates the number of times students answer questions t i , and the final formula of students' positive answer rate is (6) combined with the positive answer rate in Step 6.…”

Section: Algorithm Design Of Tdina Modelmentioning

confidence: 99%

Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis

Zhou

2022

Journal of Healthcare Engineering

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With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.

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Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs)

2023

Psychometrika

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A Class of Cognitive Diagnosis Models for Polytomous Data

Cited by 4 publications

References 40 publications

The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments

The Restricted DINA Model: A Comprehensive Cognitive Diagnostic Model for Classroom-Level Assessments

Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis

Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs)

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