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This summary is machine-generated.

This study predicts complex problem-solving (CPS) success and duration using process data. Event history analysis reveals patterns in problem-solving, aiding in skill development and automated interventions.

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Area of Science:

  • Educational Psychology
  • Cognitive Science
  • Data Science

Background:

  • Complex problem-solving (CPS) is a critical 21st-century skill.
  • Individual CPS ability can be inferred from task completion processes.
  • Understanding problem-solving patterns is key to skill development.

Purpose of the Study:

  • To predict the duration and success of complex problem-solving using process data.
  • To investigate the information contained within process data regarding CPS ability.
  • To identify successful CPS patterns and time-to-completion predictors.

Main Methods:

  • Utilizing event history analysis models.
  • Analyzing computer log files for recorded process data.
  • Applying models to real-world data from the 2012 PISA study.

Main Results:

  • Demonstrated the feasibility of predicting CPS outcomes from process data.
  • Identified specific problem-solving patterns associated with higher success rates.
  • Developed models that can predict remaining task completion time.

Conclusions:

  • Process data offers valuable insights into individual CPS abilities.
  • The developed models can inform the design of automated interventions for CPS training.
  • Findings contribute to a deeper understanding of cognitive processes in complex problem-solving.