A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills

Chen, Yinghan; Culpepper, Steven Andrew; Wang, Shiyu; Douglas, Jeffrey A.

doi:10.1177/0146621617721250

Cited by 63 publications

(81 citation statements)

References 17 publications

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“…Currently, there are many applications use cross-sectional LDMs to diagnose individuals’ learning status in the field of mathematics because the structure of mathematical attributes is relative clear to be identified, such as fraction calculations ( Tatsuoka, 1983 ; Wu, 2019 ), linear algebraic equations ( Birenbaum et al, 1993 ), and spatial rotations ( Chen et al, 2018 ; Wang et al, 2018 ). Some studies also apply cross-sectional LDMs to analyze data from large-scale mathematical assessments (e.g., George and Robitzsch, 2018 ; Park et al, 2018 ; Zhan et al, 2018 ; Wu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The Development of an Instrument for Longitudinal Learning Diagnosis of Rational Number Operations Based on Parallel Tests

Tang

Zhan

2020

Front. Psychol.

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The precondition of the measurement of longitudinal learning is a high-quality instrument for longitudinal learning diagnosis. This study developed an instrument for longitudinal learning diagnosis of rational number operations. In order to provide a reference for practitioners to develop the instrument for longitudinal learning diagnosis, the development process was presented step by step. The development process contains three main phases, the Q-matrix construction and item development, the preliminary/pilot test for item quality monitoring, and the formal test for test quality control. The results of this study indicate that (a) both the overall quality of the tests and the quality of each item are good enough and that (b) the three tests meet the requirements of parallel tests, which can be used as an instrument for longitudinal learning diagnosis to track students’ learning.

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Section: Introductionmentioning

confidence: 99%

The Development of an Instrument for Longitudinal Learning Diagnosis of Rational Number Operations Based on Parallel Tests

Tang

Zhan

2020

Front. Psychol.

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“…The traditional DCMs are useful to classify attribute profiles at a given point in time. Recently research has begun to consider the role of DCMs to track learning and skill acquisition in a longitudinal fashion (Kaya and Leite, 2016;Li et al, 2016;Wang et al, 2017Wang et al, , 2018Chen et al, 2018b;Zhan et al, 2019). In this type of research, the multidimensional binary latent skills for each student are assumed to be time-dependent and the purpose is to track the change of these binary skills overtime.…”

Section: Dcms and Dynamic Dcms For Response Times And Response Accuracymentioning

confidence: 99%

“…These psychometric models have been used to design assessments that measure fine-grained skills or latent attributes across various domains, such as math skills (Bradshaw et al, 2014) and depression (Wang et al, 2019a). In addition to these applications of cross-sectional cognitive diagnostic assessment, the recently development of dynamic DCMs (e.g., Kaya and Leite, 2016;Li et al, 2016;Wang et al, 2017Wang et al, , 2018Chen et al, 2018b;Zhan et al, 2019) enable the possibility of developing longitudinal cognitive diagnostic assessments to track skill learning and skill acquisition over time. This current study serve as the first attempt to develop the learning program within the longitudinal cognitive diagnostic assessment framework.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Wang

et al. 2020

Front. Psychol.

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This study reported on development and evaluation of a learning program that integrated a multidimensional diagnostic assessment with two different learning interventions with the aim to diagnose and improve three-dimensional mental rotation skills. The multidimensional assessment was built upon the Diagnostic Classification Model (DCM) framework that can report the binary mastery on each specific rotation skill. The two learning interventions were designed to train students to use a holistic rotation strategy and a combined analytic and holistic strategy, respectively. The program was evaluated through an experiment paired with multiple exploratory and confirmatory statistical analysis. Particularly, the recently proposed joint models for response times and response accuracy within dynamic DCM framework is applied to assess the effectiveness of the learning interventions. Compared with the traditional assessment on spatial skills, where the tests are timed and number correct is reported as a measure for test-takers' performances, the developed dynamic diagnostic assessment can provide an informative estimate of the learning trajectory for each participant in terms of the strengths and weaknesses in four fine-grained spatial rotation skills over time. Compared with an earlier study that provided initial evidence of the effectiveness of building a multidimensional diagnostic assessment with training tools, the present study improved the assessment and learning intervention design. Using both response times and response accuracy, thus current study additionally evaluated the newly developed program by investigating the effectiveness of two interventions across gender, country and rotation strategy.

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“…The test was developed based on an extended version of the Purdue Spatial Visualization Test (PSVT; Yoon, ) and was analysed by a DINA model in the literature (e.g. Chen, Culpepper, Wang, & Douglas, ; Culpepper, ; Wang, Yang, Culpepper, & Douglas, ).…”

Section: An Empirical Examplementioning

confidence: 99%

Two‐Stage maximum likelihood estimation in the misspecified restricted latent class model

Wang

2017

Brit J Math & Statis

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The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.

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A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills

Cited by 63 publications

References 17 publications

The Development of an Instrument for Longitudinal Learning Diagnosis of Rational Number Operations Based on Parallel Tests

The Development of an Instrument for Longitudinal Learning Diagnosis of Rational Number Operations Based on Parallel Tests

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Two‐Stage maximum likelihood estimation in the misspecified restricted latent class model

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