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A Multisensor Dataset of South Asian Post-Graduate Students Working on Mental Rotation Tasks.

Ashwin T S1, Suraj Ranganath2, Kabyashree Khanikar2

  • 1Indian Institute of Technology Bombay, IDP in Educational Technology, Mumbai, 400076, India. ashwindixit9@gmail.com.

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Summary
This summary is machine-generated.

This study explores spatial thinking and mental rotation using sensor data. Findings offer insights into cognitive strategies, emotional responses, and learning analytics in STEM education.

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Area of Science:

  • Cognitive Science
  • Educational Technology
  • Neuroscience

Background:

  • Spatial thinking, particularly mental rotation, is crucial for STEM learning.
  • Sensor-based approaches offer new avenues for studying cognition and affect.
  • Multimodal data collection enhances understanding of complex cognitive tasks.

Purpose of the Study:

  • To investigate cognitive and affective responses during mental rotation tasks.
  • To explore the impact of feedback and time restrictions on performance.
  • To create a rich dataset for spatial cognition and learning analytics research.

Main Methods:

  • Collected multimodal sensor data (galvanic skin response, EEG, facial expressions, gaze) from 38 participants.
  • Recorded task performance (response times, correctness, difficulty) and self-reports.
  • Implemented three experimental conditions: no time/no feedback, no time/feedback, time/no feedback.

Main Results:

  • The dataset enables analysis of strategy selection, cognitive flexibility, and affective states.
  • Potential to reveal how feedback influences learning and engagement in mental rotation.
  • Facilitates research on individual differences in spatial abilities.

Conclusions:

  • The multimodal dataset provides a comprehensive resource for spatial thinking research.
  • Offers valuable insights for the learning analytics community regarding feedback mechanisms.
  • Supports deeper understanding of cognitive processes in STEM-related tasks.