Mingmin He
ABSTRACT
Mental health education plays an important role for psychological health of college students. It is worth exploring how to effectively carry out mental health education online courses, give full play to the advantages of online courses. With the rapid development of robotics and intelligent control technology, online education can be completed depending on different intelligent devices. However, it is still a challenge to implement better online education programs on multiple intelligent devices and even educational robots. To solve this challenge, combining the artificial intelligence and intelligent control technologies, we propose a self-supervised interest recommendation based intelligent system design, which can be applied to mental health education and easily adapted to different intelligent devices. In particular, we introduce a self-supervised method to capture the students’ interests when they learn about the mental health courses, and reinforce their knowledge by recommending top-K courses that are likely to interest them most. The experiment results show the model performance, and also demonstrate the effectiveness of the proposed intelligent system.
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