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Markov Decision Process Measurement Model

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  • Michelle M. LaMar

    (Educational Testing Service)

Abstract

Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

Suggested Citation

  • Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    • Handle: RePEc:spr:psycho:v:83:y:2018:i:1:d:10.1007_s11336-017-9570-0
    DOI: 10.1007/s11336-017-9570-0
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    References listed on IDEAS

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    1. Laine Bradshaw & Jonathan Templin, 2014. "Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 403-425, July.
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    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Susu Zhang & Zhi Wang & Jitong Qi & Jingchen Liu & Zhiliang Ying, 2023. "Accurate Assessment via Process Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 76-97, March.
    2. Peida Zhan & Xin Qiao, 2022. "DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1529-1547, December.
    3. Yunxiao Chen, 2020. "A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 1052-1075, December.
    4. Pujue Wang & Hongyun Liu, 2024. "Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 877-902, September.
    5. Esther Ulitzsch & Qiwei He & Vincent Ulitzsch & Hendrik Molter & André Nichterlein & Rolf Niedermeier & Steffi Pohl, 2021. "Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 190-214, March.

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