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Conference Paper: Cognitive Diagnosis Modeling: A General Framework Approach and Its Implementation in R
Title | Cognitive Diagnosis Modeling: A General Framework Approach and Its Implementation in R |
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Authors | |
Issue Date | 2019 |
Publisher | Department of Statistics, TU Dortmund University. |
Citation | Pre-Workshop Tutorial, International Workshop on Cognitive Diagnosis Models 2019 (CDM2019), Dortmund, Germany, 24-26 July.2019
How to Cite? |
Abstract | This tutorial aims to provide participants the necessary practical experience to use cognitive diagnosis models (CDMs) in applied settings. It will also highlight the theoretical underpinnings needed for the proper use of CDMs.
In this tutorial, participants will be introduced to a proportional reasoning (PR) assessment that was developed from scratch using a CDM paradigm. Participants will get opportunities to work with PR assessment-based data. Moreover, they will learn how to use GDINA , an R package developed by Ma and de la Torre (in press) for a series of CDM analyses (e.g., model calibration, CDM evaluation at item and test levels, Q-matrix validation, differential item functioning analysis). To ensure the proper use of CDMs, the theoretical bases for these analyses will be discussed.
The intended audience of the tutorial includes anyone interested in CDMs who has some familiarity with item response theory and the R programming language. No previous knowledge of CDM is required. By the end of the session, participants are expected to have a basic understanding of the theoretical underpinnings of CDM, as well as the ability to conduct various CDM analyses using the GDINA package. Participants will be requested to bring their laptops for the GDINA package hands-on exercises.
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Persistent Identifier | http://hdl.handle.net/10722/295715 |
DC Field | Value | Language |
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dc.contributor.author | de la Torre, J | - |
dc.contributor.author | Sorrel, MA | - |
dc.date.accessioned | 2021-02-05T09:17:17Z | - |
dc.date.available | 2021-02-05T09:17:17Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Pre-Workshop Tutorial, International Workshop on Cognitive Diagnosis Models 2019 (CDM2019), Dortmund, Germany, 24-26 July.2019 | - |
dc.identifier.uri | http://hdl.handle.net/10722/295715 | - |
dc.description.abstract | This tutorial aims to provide participants the necessary practical experience to use cognitive diagnosis models (CDMs) in applied settings. It will also highlight the theoretical underpinnings needed for the proper use of CDMs. In this tutorial, participants will be introduced to a proportional reasoning (PR) assessment that was developed from scratch using a CDM paradigm. Participants will get opportunities to work with PR assessment-based data. Moreover, they will learn how to use GDINA , an R package developed by Ma and de la Torre (in press) for a series of CDM analyses (e.g., model calibration, CDM evaluation at item and test levels, Q-matrix validation, differential item functioning analysis). To ensure the proper use of CDMs, the theoretical bases for these analyses will be discussed. The intended audience of the tutorial includes anyone interested in CDMs who has some familiarity with item response theory and the R programming language. No previous knowledge of CDM is required. By the end of the session, participants are expected to have a basic understanding of the theoretical underpinnings of CDM, as well as the ability to conduct various CDM analyses using the GDINA package. Participants will be requested to bring their laptops for the GDINA package hands-on exercises. | - |
dc.language | eng | - |
dc.publisher | Department of Statistics, TU Dortmund University. | - |
dc.relation.ispartof | International Workshop on Cognitive Diagnosis Models: Theory meets Application - Pre-Workshop Tutorial | - |
dc.title | Cognitive Diagnosis Modeling: A General Framework Approach and Its Implementation in R | - |
dc.type | Conference_Paper | - |
dc.identifier.email | de la Torre, J: jdltorre@hku.hk | - |
dc.identifier.authority | de la Torre, J=rp02159 | - |
dc.identifier.hkuros | 302336 | - |
dc.publisher.place | Germany | - |