IEyeGASE: An Intelligent Eye Gaze-Based Assessment System for Deeper Insights into Learner Performance

Area of Science:

• Educational Technology
• Human-Computer Interaction
• Cognitive Science

Background:

• Online learning necessitates effective methods for assessing learner comprehension beyond traditional testing.
• Existing online assessments struggle to capture nuanced behavioral aspects crucial for effective learning.
• Tutors require tools to gauge learner engagement, confidence, and knowledge acquisition in digital environments.

Purpose of the Study:

• To introduce an intelligent eye gaze-based assessment system (IEyeGASE) for online learning.
• To address the limitations of current online assessments in quantifying learner behaviors.
• To provide educators with deeper insights into learner cognitive and engagement patterns.

Main Methods:

• Development of an intelligent eye gaze-based assessment system (IEyeGASE).
• Integration capabilities with existing online assessment platforms.
• Utilizing eye-tracking technology to monitor learner behavior during assessments.

Main Results:

• IEyeGASE measures critical learner aspects like engagement, confidence, and knowledge acquisition.
• The system provides real-time insights into behavioral patterns, including inattentional blindness.
• Demonstrates potential for quantifying aspects previously difficult to assess in online settings.

Conclusions:

• IEyeGASE offers a novel approach to enhance the depth of online learner assessment.
• The system empowers educators to identify learning challenges and implement timely interventions.
• Eye gaze analysis in online assessments can significantly improve educational feedback and support.