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Statistical analysis of complex problem-solving process data: an event history analysis approach

Author

Listed:
  • Chen, Yunxiao
  • Li, Xiaoou
  • Liu, Jingchen
  • Ying, Zhiliang

Abstract

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill. Individuals' processes of solving crucial complex problems may contain substantial information about their CPS ability. In this paper, we consider the prediction of duration and final outcome (i.e., success/failure) of solving a complex problem during task completion process, by making use of process data recorded in computer log files. Solving this problem may help answer questions like "how much information about an individual's CPS ability is contained in the process data?," "what CPS patterns will yield a higher chance of success?," and "what CPS patterns predict the remaining time for task completion?" We propose an event history analysis model for this prediction problem. The trained prediction model may provide us a better understanding of individuals' problem-solving patterns, which may eventually lead to a good design of automated interventions (e.g., providing hints) for the training of CPS ability. A real data example from the 2012 Programme for International Student Assessment (PISA) is provided for illustration.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:100871
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    File URL: http://eprints.lse.ac.uk/100871/
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Esther Ulitzsch & Qiwei He & Steffi Pohl, 2022. "Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 3-35, February.
    4. Xueying Tang & Susu Zhang & Zhi Wang & Jingchen Liu & Zhiliang Ying, 2021. "ProcData: An R Package for Process Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 1058-1083, December.

    More about this item

    Keywords

    Complex problem solving; Event history analysis; PISA data; Process data; Response time;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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