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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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    1. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
    2. Unknown, 2014. "Department Publications 2013," Publications Lists 206935, University of Minnesota, Department of Applied Economics.
    3. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    4. David Thissen & Lynne Steinberg, 1986. "A taxonomy of item response models," Psychometrika, Springer;The Psychometric Society, vol. 51(4), pages 567-577, December.
    5. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2019. "Statistical analysis of complex problem-solving process data: an event history analysis approach," LSE Research Online Documents on Economics 100871, London School of Economics and Political Science, LSE Library.
    6. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    7. Yunxiao Chen & Xiaoou Li & Siliang Zhang, 2019. "Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 124-146, March.
    8. Chen, Yunxiao & Li, Xiaoou & Zhang, Siliang, 2019. "Structured latent factor analysis for large-scale data: identifiability, estimability, and their implications," LSE Research Online Documents on Economics 101122, London School of Economics and Political Science, LSE Library.
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