Key result
An SVM model using wearable physiological recordings predicts student learning immersion with ~90% accuracy.
Why the study?
Traditional methods like questionnaires to evaluate student learning immersion are influenced by subjective factors, creating a need for objective physiological evaluation models.
An SVM-based model using EEG and PPG signals can predict students' learning immersion experience with high accuracy, reducing reliance on subjective questionnaires.
Offers objective assessment of learning immersion; leaves open validation in larger cohorts before educational use.
The increasing development in the field of biosensing technologies makes it feasible to monitor students’ physiological signals in natural learning scenarios. With the rise of mobile learning, educators are attaching greater importance to the learning immersion experience of students, especially with the global background of COVID-19. However, traditional methods, such as questionnaires and scales, to evaluate the learning immersion experience are greatly influenced by individuals’ subjective factors. Herein, our research aims to explore the relationship and mechanism between human physiological recordings and learning immersion experiences to eliminate subjectivity as much as possible. We collected electroencephalogram and photoplethysmographic signals, as well as self-reports on the immersive experience of thirty-seven college students during virtual reality and online learning to form the fundamental feature set. Then, we proposed an evaluation model based on a support vector machine and got a precision accuracy of 89.72%. Our research results provide evidence supporting the possibility of predicting students’ learning immersion experience by their EEGs and PPGs.
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Guo et al. (2022) studied Learning immersion experience (n=37). Virtual reality (VR) learning environment vs. Online learning environment was evaluated on Prediction accuracy of learning immersion experience (LIE) levels using SVM model. An evaluation model based on a support vector machine and wearable physiological recordings predicted students' learning immersion experience with a precision accuracy of 89.72%.
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