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Using VR in Adaptive Teaching the Optimal Use of Water and Energy to Children

Nasirian, N ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1109/ICRoM64545.2024.10903570
  3. Publisher: 2024
  4. Abstract:
  5. As global population growth and technological advancements increase the demand for natural resources, the optimal use of water and energy is becoming essential, especially as these resources face overconsumption and climate change-related scarcity. Educating children about resource conservation is critical for fostering environmental awareness in future generations. In response, we developed a game-based learning environment that uses Virtual Reality (VR) and adaptive learning algorithms to teach children aged 7-9 about water and energy conservation. Sixteen children (8 boys and 8 girls) participated in two - 15-minute VR sessions on separate days. Between these sessions, participants received a - 5-minute interactive lesson with the TABAN2 social robot, designed to reinforce key conservation concepts. Our analysis showed no significant differences in performance between boys and girls. However, participants demonstrated marked improvement from the first to the second VR session: they completed levels more quickly, answered more questions correctly, and required less assistance, as shown by significant t-test results (p < 0.05). These findings suggest that combining VR with adaptive learning and social robotics can enhance learning outcomes and motivate young learners to engage in conservation education. © 2024 IEEE
  6. Keywords:
  7. Virtual Reality ; Adversarial machine learning ; Contrastive Learning ; Federated learning ; Microrobots ; Robot learning ; Adaptive learning ; Environmental awareness ; Global population ; Humans-robot interactions ; Oculus quest 2 ; Population growth ; Questionnaire ; Resource conservation ; Technological advancement ; Water and energies ; Virtual environments
  8. Source: ICRoM 2024 - 12th RSI International Conference on Robotics and Mechatronics ; 2024 , Pages 244-249 ; 979-833152973-4 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/10903570