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Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the

Fatma Nur Aydin1, Kubra Atalay Kabasakal2, Ismail Dilek3

  • 1Ministry of National Education, Ankara 06530, Türkiye.

Journal of Intelligence
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

Process and result data predict complex problem-solving skills moderately to well. Mathematical literacy and the vary-one-thing-at-a-time strategy were key predictors in this Programme for International Student Assessment (PISA) analysis.

Keywords:
complex problem solvingconditional gradient boosting algorithmlog filesprocess dataresult data

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Area of Science:

  • Educational Psychology
  • Data Science
  • Cognitive Science

Background:

  • Complex problem-solving skills are crucial in modern education and workplaces.
  • Assessing these skills often relies on outcome measures, but process data offers deeper insights.
  • Previous research has explored various predictors, but the combined predictive power of process and result data remains an active area of investigation.

Purpose of the Study:

  • To evaluate the predictive performance of process data versus result data for complex problem-solving skills.
  • To determine the combined predictive power of both data types.
  • To identify key variables within process and result data that contribute most to predicting performance.

Main Methods:

  • Utilized data from 915 participants in the 2012 Programme for International Student Assessment (PISA).
  • Employed the conditional gradient boosting algorithm to analyze predictive performance.
  • Extracted process data from log files (climate control unit task) and result data from cognitive/affective attributes.

Main Results:

  • Process data showed moderate predictive performance; result data showed moderate-to-good performance.
  • The combination of process and result data achieved good predictive performance.
  • Key predictors included vary-one-thing-at-a-time (VOTAT) strategy and total time (process data); mathematical and reading literacy (result data); mathematical literacy and VOTAT strategy (combined data).

Conclusions:

  • Integrating process and result data enhances the prediction of complex problem-solving skills.
  • Mathematical literacy is a significant predictor across different data types.
  • Understanding cognitive strategies like VOTAT alongside domain knowledge is vital for assessing problem-solving abilities.