Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models

Ma, Chenchen; Ouyang, Jing; Xu, Gongjun

doi:10.1007/s11336-022-09867-5

Cited by 16 publications

(9 citation statements)

References 47 publications

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“…Last, the TH-DCM investigated in this study assumes a known attribute hierarchy, but in practice, the attribute hierarchy may not be known or may be misspecified. A few methods have been proposed to identify the attribute hierarchy empirically (e.g., C. Wang & Lu, 2021; C. Ma et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

A Testlet Diagnostic Classification Model with Attribute Hierarchies

Wang

Xiao

2023

Applied Psychological Measurement

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In this article, a testlet hierarchical diagnostic classification model (TH-DCM) was introduced to take both attribute hierarchies and item bundles into account. The expectation-maximization algorithm with an analytic dimension reduction technique was used for parameter estimation. A simulation study was conducted to assess the parameter recovery of the proposed model under varied conditions, and to compare TH-DCM with testlet higher-order CDM (THO-DCM; Hansen, M. (2013). Hierarchical item response models for cognitive diagnosis (Unpublished doctoral dissertation). UCLA; Zhan, P., Li, X., Wang, W.-C., Bian, Y., & Wang, L. (2015). The multidimensional testlet-effect cognitive diagnostic models. Acta Psychologica Sinica, 47(5), 689. https://doi.org/10.3724/SP.J.1041.2015.00689 ). Results showed that (1) ignoring large testlet effects worsened parameter recovery, (2) DCMs assuming equal testlet effects within each testlet performed as well as the testlet model assuming unequal testlet effects under most conditions, (3) misspecifications in joint attribute distribution had an differential impact on parameter recovery, and (4) THO-DCM seems to be a robust alternative to TH-DCM under some hierarchical structures. A set of real data was also analyzed for illustration.

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Section: Discussionmentioning

confidence: 99%

A Testlet Diagnostic Classification Model with Attribute Hierarchies

Wang

Xiao

2023

Applied Psychological Measurement

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“…For relationships among different attributes, some commonly used CDMs assume that all the skills are independent. However, in practice, researchers pay more attention to attribute dependencies instead of independent structures among all latent attributes (Ma et al., 2022; Zhang & Wang, 2020). For this purpose, four different types of hierarchical structures were proposed, including linear, convergent, divergent, and unstructured hierarchies (Leighton et al., 2004; Leighton & Gierl, 2007; von Davier & Lee, 2019).…”

Section: Tracking Learning Progress Over Timementioning

confidence: 99%

Knowledge Integration in Science Learning: Tracking Students' Knowledge Development and Skill Acquisition with Cognitive Diagnosis Models

Xu,

Ren,

Zhang

et al. 2024

Educational Measurement

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In scientific literacy, knowledge integration (KI) is a scaffolding‐based theory to assist students' scientific inquiry learning. To drive students to be self‐directed, many courses have been developed based on KI framework. However, few efforts have been made to evaluate the outcome of students' learning under KI instruction. Moreover, finer‐grained information has been pursued to better understand students' learning and how it progresses over time. In this article, a normative procedure of building and choosing cognitive diagnosis models (CDMs) and attribute hierarchies was formulated under KI theory. We examined the utility of CDMs for evaluating students' knowledge status in KI learning. The results of the data analysis confirmed an intuitive assumption of the hierarchical structure of KI components. Furthermore, analysis of pre‐ and posttests using a higher‐order, hidden Markov model tracked students' skill acquisition while integrating knowledge. Results showed that students make significant progress after using the web‐based inquiry science environment (WISE) platform.

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“…If the number of attributes is large, the LCDM will attempt to estimate 2 A attribute profiles, including those that do not exist. This will likely result in incorrect or unstable parameter estimates and attribute profile misclassifications (e.g., Hu & Templin, 2020;Ma, Ouyang, & Xu, 2023;Wang & Lu, 2021). Templin and Bradshaw (2014) obtained by placing constraints on the structural model to reflect the hypothesis that certain attribute profiles are implausible.…”

Section: Lcdm Structural Model and Attribute Hierarchiesmentioning

confidence: 99%

“…The HDCM has been found to be a suitable alternative for modeling DCMs when an attribute hierarchy exists; however, a limitation of this model is that attribute profiles that do not follow the structure of the attribute hierarchy are assumed to have zero probability of existing in the population, inducing what we call a strict hierarchy. Recent research by Ma, Ouyang, and Xu (2023) and Wang and Lu (2021), among others, have explored alternative methods for modeling attribute structures, however their methods also impose the requirement of a strict hierarchical structure. The approach presented in this paper differs from these works as we relax the assumption of a strict attribute hierarchy by allowing unlikely attribute profiles to have a non-zero probability of existing in the population (i.e., a "malleable" hierarchy) by parameterizing the structural model as a Bayesian network.…”

Section: Lcdm Structural Model and Attribute Hierarchiesmentioning

confidence: 99%

Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

Martinez¹,

Templin²

2023

Preprint

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This paper demonstrates the process of invariance testing in diagnostic classification models in the presence of attribute hierarchies via an extension of the log-linear cognitive diagnosis model (LCDM). This extension allows researchers to test for measurement (item) invariance as well as attribute (structural) invariance simultaneously in a single analysis. The structural model of the LCDM is parameterized as a Bayesian network which allows attribute hierarchies to be modeled and tested for attribute invariance via a series of latent regression models. We illustrate the steps for carrying out the invariance analyses through an in-depth case study with an empirical dataset and provide JAGS code for carrying out the analysis within a Bayesian framework. The analysis revealed that a subset of the items exhibit partial invariance and evidence of full invariance was found at the structural level.

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Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models

Cited by 16 publications

References 47 publications

A Testlet Diagnostic Classification Model with Attribute Hierarchies

A Testlet Diagnostic Classification Model with Attribute Hierarchies

Knowledge Integration in Science Learning: Tracking Students' Knowledge Development and Skill Acquisition with Cognitive Diagnosis Models

Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

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