A Virtual Reality and Online Learning Immersion Experience Evaluation Model Based on SVM and Wearable Recordings
| Autor: | Junqi Guo, Boxin Wan, Hao Wu, Ziyun Zhao, Wenshan Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Hardware and Architecture Control and Systems Engineering Signal Processing virtual reality (VR) online learning wearable biosensing recordings photoplethysmographic (PPG) electroencephalogram (EEG) college students learning immersion experience (LIE) machine learning support vector machine (SVM) Electrical and Electronic Engineering |
| Zdroj: | Electronics; Volume 11; Issue 9; Pages: 1429 |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics11091429 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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