Implementation of Cognitive Diagnosis Modeling using the GDINA R Package

Torre, Jimmy de la; Akbay, Lokman

doi:10.14689/ejer.2019.80.9

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Simply put, the Q-matrix is to calibrate which attributes (e.g., skills and knowledge) are examined for each topic. The Q-matrix is typically developed by domain experts and assumed to be correct in the following CDM analyses (Ma and de la Torre, 2020). The second element is a list of models used to identify the potential cognitive characteristics or skill mastery patterns of students.…”

Section: Cognitive Diagnosis Modelmentioning

confidence: 99%

“…Attributes are a set of fine-grained skills typically designed to provide diagnostic information about the strengths and weaknesses of students. The major goal of CDM analyses is to infer the attribute profiles of students from their item responses (Ma and de la Torre, 2020). After mastering the attributes and characteristics of students, teachers can conduct targeted teaching.…”

Section: Attributes In Cdmmentioning

confidence: 99%

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International Comparative Study of Statistics Learning Trajectories Based on PISA Data on Cognitive Diagnostic Models

2021

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The cognitive diagnosis model is an emerging evaluation theory. The mastery of fine-grained knowledge points of students can be obtained via the cognitive diagnostic model (CDM), which can subsequently describe the learning trajectory. The latter is a description of the learning progress of students in a specific area, through which teaching and learning can be linked. This research is based on nine statistical items in the Program for International Student Assessment (PISA) 2012 and an analysis of the response data of 30,092 students from 14 countries from four attributes based on CDM. Then, it obtains the learning trajectory of students in statistical knowledge. The study found that Bulgaria, Costa Rica, Peru, Mexico, and Serbia have the same learning trajectories. The learning trajectories of almost 14 countries are as follows: (1) uncertainty, (2) data handling, (3) statistical chart, and (4) average.

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Section: Cognitive Diagnosis Modelmentioning

confidence: 99%

Section: Attributes In Cdmmentioning

confidence: 99%

International Comparative Study of Statistics Learning Trajectories Based on PISA Data on Cognitive Diagnostic Models

2021

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“…Suppose this hypothetical item intended to measure only the "Prevention" attribute (identified as 000010), but a "hidden" cognitive task was necessary to solve the item: diagnosing hypertension. In this case, the Q-matrix validation procedure would have correctly identified the "hidden" cognitive process, suggesting the latent class 001010 for this item in the final Q-matrix with the inclusion of the attribute "Clinical diagnosis" (de la Torre & Akbay, 2019 ; de la Torre & Minchen, 2014 ; Ma, 2019 ).…”

Section: Introducing Cognitive Diagnostic Modellingmentioning

confidence: 92%

“…The Shiny graphic user interface provided by the GDINA package makes it more suitable for the less experienced reader who might not want to learn how to write scripts in R to run such analyses. I can reassure the readers willing to do their first cognitive diagnostic modelling analysis that they can find instructions on installing and using the GDINA package and its Shiny graphic user interface in a couple of open-access papers (de la Torre & Akbay, 2019 ; Rupp & van Rijn, 2018 ). An online interactive course module on cognitive diagnostic modelling is available free of charge for the reader interested in studying this novel psychometric approach in greater depth (Ma & de la Torre, 2019 ).…”

Section: Developing Your Own Cognitive Diagnostic Modelsmentioning

confidence: 99%

Cognitive diagnostic modelling in healthcare professions education: an eye-opener

Collares

2022

Adv in Health Sci Educ

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Criticisms about psychometric paradigms currently used in healthcare professions education include claims of reductionism, objectification, and poor compliance with assumptions. Nevertheless, perhaps the most crucial criticism comes from learners' difficulty in interpreting and making meaningful use of summative scores and the potentially detrimental impact these scores have on learners. The term "post-psychometric era" has become popular, despite persisting calls for the sensible use of modern psychometrics. In recent years, cognitive diagnostic modelling has emerged as a new psychometric paradigm capable of providing meaningful diagnostic feedback. Cognitive diagnostic modelling allows the classification of examinees in multiple cognitive attributes. This measurement is obtained by modelling these attributes as categorical, discrete latent variables. Furthermore, items can reflect more than one latent variable simultaneously. The interactions between latent variables can be modelled with flexibility, allowing a unique perspective on complex cognitive processes. These characteristic features of cognitive diagnostic modelling enable diagnostic classification over a large number of constructs of interest, preventing the necessity of providing numerical scores as feedback to test takers. This paper provides an overview of cognitive diagnostic modelling, including an introduction to its foundations and illustrating potential applications, to help teachers be involved in developing and evaluating assessment tools used in healthcare professions education. Cognitive diagnosis may represent a revolutionary new psychometric paradigm, overcoming the known limitations found in frequently used psychometric approaches, offering the possibility of robust qualitative feedback and better alignment with competency-based curricula and modern programmatic assessment frameworks.

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Observed score reliability indices in diagnostic classification models

Yamaguchi

Templin

2021

Behaviormetrika

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Quantifying the reliability of latent variable estimates in diagnostic classification models has been a difficult topic, complicated by the classification-based nature of these models. In this study, we derive observed score reliability indices based on diagnostic classification models as an extension of classical test theory-based reliability. Additionally, we derive conditional observed sum-and sub-score distributions. In this manner, various conditional expectations and conditional standard error of measurement estimates can be calculated for both sum-and sub-scores of a test. The proposed methods provide a variety of expectations and standard errors for attribute estimates, which we demonstrate with an analysis of an empirical test. Moreover, a simulation study revealed the proposed sub-score based reliability index was correlated to a previously developed attribute mastery reliability index.

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Implementation of Cognitive Diagnosis Modeling using the GDINA R Package

Cited by 5 publications

References 9 publications

International Comparative Study of Statistics Learning Trajectories Based on PISA Data on Cognitive Diagnostic Models

International Comparative Study of Statistics Learning Trajectories Based on PISA Data on Cognitive Diagnostic Models

Cognitive diagnostic modelling in healthcare professions education: an eye-opener

Observed score reliability indices in diagnostic classification models

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