Analysis Platform of Student Learning Data Combined with Computer Vision
Xinghui Ding, Zhijun Zhang, Zhaohui Yang, Zhiyun Yu · 2022
At present, most teaching platforms based on artificial intelligence are designed to launch learning tasks for students. However, those teaching platforms are less to analyze the knowledge mastery of students and less to carry out personalized and accurate teaching according to the learning situation. Teachers using those platforms generally check the learning situation of the whole student in a coarse granularity, which will lead to the lack of data analysis and learning situation of a single student, and therefore there is no feedback to adjust the teaching strategy. Those factors contribute to the insufficiency of communication and interaction between teachers and students. The effect of online teaching is inferior to offline teaching. In this paper, we proposed a learning platform that combines artificial intelligence. This platform aims to strengthen teachers' control over students' learning process and help teachers improve their teaching strategies. This paper combines deep learning technology with computer vision to collect data and analyze students' learning status, using machine algorithms to achieve identity binding and data feedback. Finally, we also established the environment for big data for the future expansion of data analysis functions.