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Subtask analysis of process data through a predictive model.

Zhi Wang1, Xueying Tang2, Jingchen Liu1

  • 1Department of Statistics, Columbia University, New York City, NY, USA.

The British Journal of Mathematical and Statistical Psychology
|November 1, 2022
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Summary
This summary is machine-generated.

This study introduces an efficient computational method for analyzing complex human-computer interaction process data. The approach segments data into subtasks, enabling easier analysis of problem-solving strategies.

Keywords:
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Area of Science:

  • Cognitive Science
  • Data Science
  • Human-Computer Interaction

Background:

  • Response process data from human-computer interactions offer insights into user behavior and cognitive strategies.
  • Standard statistical methods struggle with the irregular format and complexity of this data.
  • Analyzing problem-solving strategies requires effective methods for handling process data.

Purpose of the Study:

  • To develop a computationally efficient method for the exploratory analysis of human-computer interaction process data.
  • To address the challenges posed by the complex structure and irregular format of process data.
  • To facilitate the analysis of respondents' problem-solving strategies.

Main Methods:

  • A novel approach segments lengthy individual processes into shorter, manageable subprocesses (subtasks).
  • Segmentation relies on sequential action predictability, utilizing a parsimonious predictive model.
  • Shannon entropy is integrated to enhance the segmentation based on action predictability.

Main Results:

  • The proposed method achieves complexity reduction, enabling easier clustering and interpretation of process data.
  • Simulation studies demonstrate the effectiveness and performance of the new analytical approach.
  • The method successfully facilitates exploratory analysis of process data in a real-world case study.

Conclusions:

  • The developed method offers an efficient and effective solution for analyzing complex human-computer interaction process data.
  • This approach enhances the ability to study cognitive processes and problem-solving strategies from interaction data.
  • The technique provides a valuable tool for researchers in cognitive science and human-computer interaction.