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

Author:

Chen YunxiaoORCID

Abstract

AbstractProblem 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.

Funder

National Academy of Education/Spencer Postdoctoral Fellowship

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,General Psychology

Reference37 articles.

1. Andersen, P. K., Borgan, Ø., Gill, R. D., & Keiding, N. (1988). Censoring, truncation and filtering in statistical models based on counting processes. Contemporary Mathematics, 80, 19–59.

2. Buchner, A., & Funke, J. (1993). Finite-state automata: Dynamic task environments in problem-solving research. The Quarterly Journal of Experimental Psychology, 46, 83–118.

3. Cai, L. (2010). High-dimensional exploratory item factor analysis by a Metropolis–Hastings Robbins–Monro algorithm. Psychometrika, 75, 33–57.

4. Celeux, G. (1985). The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational Statistics Quarterly, 2, 73–82.

5. Chen, Y., Li, X., Liu, J., & Ying, Z. (2019a). Statistical analysis of complex problem-solving process data: An event history analysis approach. Frontiers in Psychology, 10, 1–10.

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