Data-Driven Insights into E-Learning: A Comprehensive Review of Eye-Tracking Applications in Learning Systems

Area of Science:

• Educational Technology
• Cognitive Science
• Human-Computer Interaction

Background:

• Universities increasingly use online learning environments with technological tools to support student training and knowledge acquisition.
• Eye tracking is a key technology for studying visual attention, cognitive processes, and learning behaviors in educational settings.

Purpose of the Study:

• To conduct a scoping review of recent eye-tracking research in diverse educational populations (K-12 to university).
• To examine the potential of eye-tracking technologies, particularly when integrated with artificial intelligence (AI) techniques like machine learning.

Main Methods:

• Analysis of 54 empirical studies published in recent years.
• Focus on the applicability, strengths, and limitations of eye-tracking in education.

Main Results:

• Eye tracking provides data-driven insights into student behavior and cognitive processes, showing potential to transform educational practices.
• Integration with AI techniques like machine learning enhances the capabilities of eye-tracking for educational research.

Conclusions:

• Eye-tracking technology holds significant promise for improving educational practices through detailed analysis of learner behavior.
• Future research should focus on overcoming implementation and data analysis challenges to fully realize the benefits of eye tracking in education.