Committee Chair

Varol, Serkan

Committee Member

Goodrich, Jennifer; Akgun, Gazi

Department

Dept. of Engineering Management

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

This study explores affective engagement states in digital learning environments using synchronized EEG and eye-tracking data. EEG signals were collected from frontal channels and processed using sliding windows to extract band-power features. Frontal Alpha Asymmetry (FAA; log alpha power difference, F4–F3) and the Beta–Alpha ratio (BA; log beta/alpha power ratio) were used as affective proxies. Instead of predefined emotion labels, unsupervised clustering methods were applied to identify latent engagement patterns directly from EEG features. To ensure robustness, non-overlapping parity analysis and hold-out validation were performed. Statistical tests were conducted to compare FAA and BA values across the identified clusters. Results showed consistent differences in arousal-related features across clusters, supporting the reliability of the identified states. Eye-tracking data were synchronized with EEG windows to provide additional behavioral context. The findings demonstrate a robust multimodal framework for identifying and validating affective states during real classroom interactions.

Acknowledgments

I would like to sincerely thank my advisor, Dr. Serkan Varol, for his invaluable guidance, patience, and support throughout this research process. His mentorship, insightful feedback, and encouragement played a critical role in shaping the study and strengthening its academic quality. I am deeply grateful for his continuous support and dedication.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2026

Subject

Artificial emotional intelligence; Electroencephalography; Eye tracking

Keyword

EEG; Eye Tracking; Engagement Emotion Recognition; Learning Analytics

Document Type

Masters theses

DCMI Type

Text

Extent

xvii, 133 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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