A cognitive diagnosis model using multiple category scoring for constructed response items

Bor-Chen KUO; Chun-Hua CHEN; Magdalena Mo Ching MOK

A cognitive diagnosis model using multiple category scoring for constructed response items

Bor-Chen KUO
, Chun-Hua CHEN
, Magdalena Mo Ching MOK

Department of Psychology (PS)

Research output: Contribution to conference › Papers

Abstract

The aim of this study is to develop a cognitive diagnostic model to analyze tests with Constructed Response (CR) items. The problem solving process of CR item transferred to categorical data can provide more information than dichotomized data. However, most cognitive diagnostic models modeling categorical or dichotomized data are proposed for tests with Multiple Choice (MC) items. In this study, an extended DINA model using multiple category scoring is proposed for modeling categorical data obtained from CR items. Each response category of categorical data is a combination of multiple skills measured by the item. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters in the proposed model. Finally, results of simulation studies and analyses of real data are presented and discussed.

Original language	English
Publication status	Published - Jul 2015
Event	The 80th Annual Meeting of the Psychometric Society (IMPS) - Beijing Normal University, Beijing, China Duration: 12 Jul 2015 → 16 Jul 2015 https://www.psychometricsociety.org/imps-2015

Conference

Conference	The 80th Annual Meeting of the Psychometric Society (IMPS)
Abbreviated title	IMPS 2015
Country/Territory	China
City	Beijing
Period	12/07/15 → 16/07/15
Internet address	https://www.psychometricsociety.org/imps-2015

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title = "A cognitive diagnosis model using multiple category scoring for constructed response items",

abstract = "The aim of this study is to develop a cognitive diagnostic model to analyze tests with Constructed Response (CR) items. The problem solving process of CR item transferred to categorical data can provide more information than dichotomized data. However, most cognitive diagnostic models modeling categorical or dichotomized data are proposed for tests with Multiple Choice (MC) items. In this study, an extended DINA model using multiple category scoring is proposed for modeling categorical data obtained from CR items. Each response category of categorical data is a combination of multiple skills measured by the item. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters in the proposed model. Finally, results of simulation studies and analyses of real data are presented and discussed.",

author = "Bor-Chen KUO and Chun-Hua CHEN and MOK, \{Magdalena Mo Ching\}",

year = "2015",

month = jul,

language = "English",

note = "The 80th Annual Meeting of the Psychometric Society (IMPS), IMPS 2015 ; Conference date: 12-07-2015 Through 16-07-2015",

url = "https://www.psychometricsociety.org/imps-2015",

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T1 - A cognitive diagnosis model using multiple category scoring for constructed response items

AU - KUO, Bor-Chen

AU - CHEN, Chun-Hua

AU - MOK, Magdalena Mo Ching

PY - 2015/7

Y1 - 2015/7

N2 - The aim of this study is to develop a cognitive diagnostic model to analyze tests with Constructed Response (CR) items. The problem solving process of CR item transferred to categorical data can provide more information than dichotomized data. However, most cognitive diagnostic models modeling categorical or dichotomized data are proposed for tests with Multiple Choice (MC) items. In this study, an extended DINA model using multiple category scoring is proposed for modeling categorical data obtained from CR items. Each response category of categorical data is a combination of multiple skills measured by the item. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters in the proposed model. Finally, results of simulation studies and analyses of real data are presented and discussed.

AB - The aim of this study is to develop a cognitive diagnostic model to analyze tests with Constructed Response (CR) items. The problem solving process of CR item transferred to categorical data can provide more information than dichotomized data. However, most cognitive diagnostic models modeling categorical or dichotomized data are proposed for tests with Multiple Choice (MC) items. In this study, an extended DINA model using multiple category scoring is proposed for modeling categorical data obtained from CR items. Each response category of categorical data is a combination of multiple skills measured by the item. The Expectation-Maximization (EM) algorithm is applied to estimate the parameters in the proposed model. Finally, results of simulation studies and analyses of real data are presented and discussed.

M3 - Papers

T2 - The 80th Annual Meeting of the Psychometric Society (IMPS)

Y2 - 12 July 2015 through 16 July 2015

ER -

A cognitive diagnosis model using multiple category scoring for constructed response items

Abstract

Conference

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