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A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data

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  • Yunxiao Chen

    (London School of Economics and Political Science)

Abstract

Problem solving has been recognized as a central skill that today’s students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student’s problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students’ problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:4:d:10.1007_s11336-020-09734-1
    DOI: 10.1007/s11336-020-09734-1
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Xueying Tang, 2024. "A Latent Hidden Markov Model for Process Data," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 205-240, March.

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