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A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation.

Jamil Hussain1, Wajahat Ali Khan2, Taeho Hur3

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Summary
This summary is machine-generated.

This study introduces a novel platform for user experience (UX) measurement, enhancing accuracy through a mixed-method triangulation approach. It integrates diverse data sources to reduce bias and provide deeper user insights.

Keywords:
EEGeye-trackingfacial expressiongalvanic skin responseinteraction trackermix-method approachself-reportinguser experience evaluationuser experience measurementuser experience platform

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Area of Science:

  • Human-Computer Interaction
  • User Experience Research
  • Applied Psychology

Background:

  • User experience (UX) evaluation is crucial but challenging.
  • Existing methods often lack precision or are subjective.
  • Mixed-method triangulation offers improved accuracy but is complex to implement.

Purpose of the Study:

  • To propose a method and system for measuring overall user experience (UX) over time using triangulation.
  • To reduce subjective bias in UX assessment.
  • To validate user perceptions through objective measurements.

Main Methods:

  • Developed a platform integrating observational, physiological, and traditional UX measurements.
  • Employed a triangulation approach to combine data from multiple sensors.
  • Incorporated plug-and-play device support and advanced analytics.

Main Results:

  • The platform objectifies subjective user perceptions, reducing bias.
  • It validates user perceptions through sensor-based measurements.
  • Provides insights into UX across multiple participants over time.

Conclusions:

  • The proposed platform offers a more accurate and objective method for UX evaluation.
  • It simplifies the complex process of mixed-method triangulation for UX research.
  • Enables deeper, data-driven insights into user interactions with products.