NeuroTutor: Neural Decoding of Student Engagement During Virtual and Robotic Tutoring
The use of electroencephalography (EEG)-based analysis in educational research offers valuable insight into the dynamics of virtual and robot-based tutoring systems. This study aims to classify neural patterns associated with two distinct tutoring modalities (virtual tutor versus robot tutor) to und...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11071325/ |
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| Summary: | The use of electroencephalography (EEG)-based analysis in educational research offers valuable insight into the dynamics of virtual and robot-based tutoring systems. This study aims to classify neural patterns associated with two distinct tutoring modalities (virtual tutor versus robot tutor) to understand how different educational interfaces affect brain activity and engagement biomarkers. This classification enables adaptive learning systems that can optimize educational environments based on real-time neural responses, potentially enhancing learning outcomes by matching tutoring modalities to individual neural preferences. We examine the performance of multiple EEG features such as power spectral density, Fast Fourier Transform (FFT) magnitude coefficients, amplitude and variance, coherence, Hjorth parameters, and wavelet transform coefficients in distinguishing between these two tutoring modalities. A transformer-based binary classifier was employed to evaluate the effectiveness of these characteristics in classifying EEG data collected during interactions with both types of tutors. The wavelet transform coefficients demonstrated the highest classification accuracy of 98.75%, precision of 99.17%, recall of 98.18%, F1 score of 98.61%, and Area Under the Curve (AUC) of 1.00, indicating exceptional performance in all metrics. This high accuracy represents a significant advancement in EEG-based educational classification systems. The Hjorth parameters (90.13% accuracy, 89.91% precision, 95.45% recall) and preprocessed EEG (91.73% accuracy, 91.57% precision, 90.91% recall) also showed strong performance. Simpler features, such as amplitude and variance, exhibited limited discriminatory power with only 72.67% accuracy. These findings underscore the importance of feature engineering and robust preprocessing in EEG-based educational studies and demonstrate the feasibility of creating personalized learning environments that can adapt to individual students’ neural preferences. Limitations such as a small sample size and topic-specific focus are noted, paving the way for future research to generalize findings and optimize methodologies. |
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| ISSN: | 2169-3536 |