EyeLearn: Revolutionizing Learning Assessment Through Visual Cognition

Research Poster Engineering 2025 Graduate Exhibition

Presentation by Kevin Mekulu

Exhibition Number 28

Abstract

Learning disabilities represent a significant educational challenge affecting millions of students worldwide, yet current gold standard assessments like standardized testing and paper-based evaluations are fraught with critical limitations including cultural bias, outcome-centered evaluation, and inability to capture cognitive processes. Our novel approach leveraging eye tracking in virtual reality (VR) learning environments provides unprecedented access to previously invisible cognitive mechanisms, enabling objective, process-based assessment of learning capabilities where traditional methods fail. In our study, we developed a VR learning environment using Unity with integrated Tobii eye-tracking technology to capture participants' visual attention patterns during assembly tasks. The VR system accurately tracked gaze coordinates, fixation durations, and object interactions at 250Hz sampling rate while participants reconstructed vehicle components following a predefined sequence. We applied machine learning (ML) algorithms to analyze visual attention patterns and identify characteristic gaze behaviors associated with different learning abilities. Our analysis revealed a statistically significant increase in gaze pattern organization over time, indicating more structured visual strategies as learning progresses. The computational models successfully differentiated between learning patterns, with fast learners demonstrating targeted, non-linear scanning strategies focusing on critical components, while slower learners exhibited sequential, linear exploration patterns requiring greater visual coverage. These findings demonstrate the potential for eye-tracking data combined with advanced machine learning algorithms to automatically assess learning capabilities and identify potential learning difficulties earlier and more accurately than traditional methods.

Importance

This research represents a transformative approach to learning assessment by revealing cognitive processes that traditional methods cannot capture. By analyzing eye movements during VR-based learning tasks, we can identify learning difficulties much earlier and more objectively than conventional testing allows. The integration of eye tracking with machine learning creates a pathway to personalized learning interventions based on actual cognitive processes rather than just outcomes. This approach has profound implications for educational equity, as it eliminates cultural and linguistic biases inherent in traditional assessments. Ultimately, this work could revolutionize how we identify, understand, and support different learning styles and challenges, potentially transforming educational practices and improving outcomes for millions of students worldwide.

DEI Statement

Our research directly addresses educational inequity by reimagining how learning abilities are assessed. Traditional standardized tests and paper-based evaluations contain inherent cultural, linguistic, and socioeconomic biases that disproportionately impact underserved populations. By analyzing objective eye movement patterns rather than culturally-loaded responses, our VR-based assessment approach eliminates many barriers faced by students from diverse backgrounds. This technology shows particular promise for earlier and more accurate identification of learning differences in students who might otherwise be misclassified or overlooked due to language barriers, cultural differences, or testing anxiety. Our methodology represents a significant step toward creating truly equitable educational assessment that recognizes diverse learning approaches and provides equal opportunity for all students to demonstrate their true capabilities, regardless of background.