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Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game:

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Pupillometry, measuring cognitive load via pupil responses, can accurately assess video game difficulty. This study introduces an image filter to improve pupil size estimation, enhancing learning game difficulty classification.

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Perceived difficulty in learning tasks impacts results.
  • Gamer data (errors, time) estimate difficulty.
  • Pupillometry measures cognitive load, reflecting perceived difficulty.

Purpose of the Study:

  • Assess pupillary responses for cognitive load in video game difficulty.
  • Propose an image filter for baseline pupil size estimation.
  • Reduce screen luminescence effects on pupillometry.

Main Methods:

  • Experiment comparing proposed filter's baseline to common methods.
  • Used a classifier with pupil features to classify game difficulty.
  • Dataset included student data from a math fractions video game.

Main Results:

  • Proposed filter provides better baseline estimation.
  • Significant differences in mean pupil diameter change (MPDC) observed.
  • MPDC and peak dilation correlated with difficulty; classifier accuracy increased from 75% to 87.5% with pupillary data.

Conclusions:

  • Scrambled filter reduces screen luminescence effects on pupil size.
  • Pupillary response data significantly improve classifier accuracy for educational game difficulty.