IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/100871.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/100871/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:100871. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.