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Predictive Feature Generation and Selection Using Process Data From PISA Interactive Problem-Solving Items: An

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Summary
This summary is machine-generated.

This study introduces a method to analyze student computer interaction data from the Programme for International Student Assessment (PISA) problem-solving tasks. It identifies key interaction features that predict student performance, offering insights for educational assessment.

Keywords:
PISAfeature generationfeature selectioninteractive itemsproblem-solvingprocess datarandom forests

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Area of Science:

  • Educational assessment
  • Cognitive science
  • Data science

Background:

  • The Programme for International Student Assessment (PISA) incorporated problem-solving skills assessment in 2012.
  • Computer-based testing generates process data detailing student interactions with the assessment platform.

Purpose of the Study:

  • To develop and validate a method for generating predictive features from process data.
  • To identify which interaction features are most predictive of student responses in a problem-solving task.

Main Methods:

  • A two-stage approach was used to generate and select features from process data.
  • The random forest algorithm, a tree-based ensemble method, was employed to analyze the association between process data features and student responses.
  • Feature importance was assessed to determine predictive value.

Main Results:

  • The study successfully generated features from process data to predict student responses on the Climate Control Task.
  • The random forest model effectively identified and ranked the importance of various interaction features.

Conclusions:

  • This approach offers a novel way to analyze process data for pedagogical insights in educational assessments.
  • Understanding student interaction patterns can enhance the interpretation of problem-solving skills.