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Exploring Multiple Goals Balancing in Complex Problem Solving Based on Log Data.

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Summary
This summary is machine-generated.

Balancing multiple goals is key to complex problem solving (CPS). Competent solvers effectively clarify, prioritize, and trade-off conflicting objectives for better performance in CPS tasks.

Keywords:
K-means cluster analysiscomplex problem solvingeducational data mininglog data analysismultiple goals balancing

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Area of Science:

  • Cognitive Psychology
  • Educational Measurement
  • Artificial Intelligence

Background:

  • Complex problem solving (CPS) is a critical skill.
  • Balancing multiple goals is an under-validated aspect of CPS.
  • Understanding goal-balancing strategies is essential for improving problem-solving abilities.

Purpose of the Study:

  • To investigate goal clarification, prioritization, and balancing in complex problem solving.
  • To identify behavioral patterns of goal pursuit using process data.
  • To examine the relationship between goal pursuit strategies and CPS proficiency.

Main Methods:

  • Analysis of log data from 3,201 students on a specific CPS task ('Ticket' subtask, PISA 2012).
  • Utilized cluster analysis to identify distinct goal pursuit behaviors.
  • Employed regression analyses to link goal pursuit to task scores and CPS proficiency.

Main Results:

  • Identified 10 distinct groups based on goal pursuit behaviors.
  • Competent solvers demonstrated superior goal clarification and prioritization.
  • Effective trade-offs between conflicting goals were characteristic of high-performing solvers.

Conclusions:

  • Goal balancing is a crucial, measurable dimension of CPS.
  • Log data analysis provides valuable insights into problem-solving strategies.
  • Developing coping strategies for multiple goal conflicts can enhance CPS proficiency.