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

Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Article number486
JournalFrontiers in Psychology
Volume10
Issue numberMAR
DOIs
StatePublished - 2019

Bibliographical note

Funding Information:
This research was funded by NAEd/Spencer postdoctoral fellowship, NSF grant DMS-1712657, NSF grant SES-1826540, NSF grant IIS-1633360, and NIH grant R01GM047845

Keywords

  • Complex problem solving
  • Event history analysis
  • PISA data
  • Process data
  • Response time

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