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Cognitive diagnosis models for multiple strategies
Author(s) -
Ma Wenchao,
Guo Wenjing
Publication year - 2019
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12155
Subject(s) - matching (statistics) , maximization , computer science , set (abstract data type) , selection (genetic algorithm) , model selection , cognition , machine learning , artificial intelligence , mathematics , data mining , mathematical optimization , statistics , psychology , programming language , neuroscience
Cognitive diagnosis models ( CDM s) have been used as psychometric tools in educational assessments to estimate students’ proficiency profiles. However, most CDM s assume that all students adopt the same strategy when approaching problems in an assessment, which may not be the case in practice. This study develops a generalized multiple‐strategy CDM for dichotomous response data. The proposed model provides a unified framework to accommodate various condensation rules (e.g., conjunctive, disjunctive, and additive) and different strategy selection approaches (i.e., probability‐matching, over‐matching, and maximizing). Model parameters are estimated using the marginal maximum likelihood estimation via expectation‐maximization algorithm. Simulation studies showed that the parameters of the proposed model can be adequately recovered and that the proposed model was relatively robust to some types of model misspecifications. A set of real data was analysed as well to illustrate the use of the proposed model in practice.