A Reinforcement Learning-Based Smart Educational Environment for Higher Education

Siyong Fu

OAI: oai:igi-global.com:315019 • DOI: 10.4018/IJeC.315019

Most higher education institutions use unique technologies to improve learning activities and provide comfortable learning. Higher education in a smart education environment (SEE) uses various tools and procedures to develop a smart learning environment to improve learning efficiency. Still, these learning processes fail to analyze student knowledge and cognitive features. The inappropriate identification of student learning skills affects teaching and learning quality. This problem is overcome using digital smart classrooms that support the student learning features because social factors and student personal behavior affects learning efficiency. So, the SEE should adapt student variability factors and learning strategies. In this work, reinforcement learning (RL) is utilized to create smart and comfortable learning in a smart classroom. The RL method analyses student behavior change, learning materials, and technologies that improve the overall learning efficiency. The created smart learning classroom achieves benefits of e-learning like interactions, flexibility, and experience.