Human-robot interaction in higher education for predicting student engagement

In recent years, Human-Robot Interaction (HRI) has become emerging innovation in robotics research and development. Research in Human-Robot Interaction (HRI) aims to understand, design, and evaluate robotic systems intended for human use. Robots and humans must communicate for interaction to occur. Human-robot communication can take various forms, but the proximity of the two parties heavily influences these forms.HRI innovations entail learning, designing, and evaluating robotics systems that communicate with people. Robots connecting and speaking with humans are becoming increasingly common at home, customer service, education, and healthcare. Despite the fact that there are now many robotic instruments that teach students how to learn various subjects and activities, efficient integration of the prediction model is still under development. Therefore, this research introduces a student engagement prediction model using HRI (SEPM-HRI) in higher education. This model uses the supervised learning algorithm for student engagement prediction. The experimental results show that the proposed model can implement in a real-time scenario with the expected prediction accuracy of 98.78%. The experimental results show that the proposed SEPM-HRImodel enhances an accuracy ratio of 98.78%, interaction ratio of 95.62%, prediction ratio of 96.35%, a learning rate of 94.26%, a student engagement ratio of 93.14%, and a student participation ratio of 92.86%. This model gives the mean square error rate of 30.21% compared to other existing approaches.