Dataset Information

A Cognitive Diagnosis Model for Identifying Coexisting Skills and Misconceptions.

ABSTRACT: At present, most existing cognitive diagnosis models (CDMs) are designed to either identify the presence and absence of skills or misconceptions, but not both. This article proposes a CDM that can be used to simultaneously identify what skills and misconceptions students possess. In addition, it proposes the use of the expectation-maximization algorithm to estimate the model parameters. A simulation study is conducted to evaluate the viability of the proposed model and algorithm. Real data are analyzed to demonstrate the applicability of the proposed model, and compare it with existing CDMs. Furthermore, a real data-based simulation study is conducted to determine how the correct classification rates in the context of the proposed model can be improved. Issues related to the proposed model and future research are discussed.

SUBMITTER: Kuo BC

PROVIDER: S-EPMC5985701 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A Cognitive Diagnosis Model for Identifying Coexisting Skills and Misconceptions.

Kuo Bor-Chen BC Chen Chun-Hua CH de la Torre Jimmy J

Applied psychological measurement 20171007 3

At present, most existing cognitive diagnosis models (CDMs) are designed to either identify the presence and absence of skills or misconceptions, but not both. This article proposes a CDM that can be used to simultaneously identify what skills and misconceptions students possess. In addition, it proposes the use of the expectation-maximization algorithm to estimate the model parameters. A simulation study is conducted to evaluate the viability of the proposed model and algorithm. Real data are a ...[more]

PMID: 29881120

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Project description:Mixed pathology, particularly Alzheimer's disease with cerebrovascular lesions, is reported as the second most common cause of dementia. Research on mixed dementia typically includes people with a primary AD diagnosis and hence, little is known about the effects of co-existing amyloid pathology in people with vascular cognitive impairment (VCI). The purpose of this study was to understand whether individual differences in amyloid pathology might explain variations in cognitive impairment among individuals with clinical subcortical VCI (SVCI).Twenty-two participants with SVCI completed an (11)C Pittsburgh compound B (PIB) position emission tomography (PET) scan to quantify global amyloid deposition. Cognitive function was measured using: 1) MOCA; 2) ADAS-Cog; 3) EXIT-25; and 4) specific executive processes including a) Digits Forward and Backwards Test, b) Stroop-Colour Word Test, and c) Trail Making Test. To assess the effect of amyloid deposition on cognitive function we conducted Pearson bivariate correlations to determine which cognitive measures to include in our regression models. Cognitive variables that were significantly correlated with PIB retention values were entered in a hierarchical multiple linear regression analysis to determine the unique effect of amyloid on cognitive function. We controlled for age, education, and ApoE ?4 status.Bivariate correlation results showed that PIB binding was significantly correlated with ADAS-Cog (p < 0.01) and MOCA (p < 0.01); increased PIB binding was associated with worse cognitive function on both cognitive measures. PIB binding was not significantly correlated with the EXIT-25 or with specific executive processes (p > 0.05). Regression analyses controlling for age, education, and ApoE ?4 status indicated an independent association between PIB retention and the ADAS-Cog (adjusted R-square change of 15.0%, Sig F Change = 0.03). PIB retention was also independently associated with MOCA scores (adjusted R-Square Change of 27.0%, Sig F Change = 0.02).We found that increased global amyloid deposition was significantly associated with greater memory and executive dysfunctions as measured by the ADAS-Cog and MOCA. Our findings point to the important role of co-existing amyloid deposition for cognitive function in those with a primary SVCI diagnosis. As such, therapeutic approaches targeting SVCI must consider the potential role of amyloid for the optimal care of those with mixed dementia.NCT01027858.

Dataset Information

A Cognitive Diagnosis Model for Identifying Coexisting Skills and Misconceptions.

Publications

A Cognitive Diagnosis Model for Identifying Coexisting Skills and Misconceptions.

Similar Datasets

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