Teaching Video Recommendation Based on Student Evaluation

Abstract

At present, online education gradually changes the traditional education model, but the development of students is diverse. Because of the lack of interaction, teaching videos can hardly meet the individual needs of students. Nowadays, online teaching videos are mixed and it is very difficult for students and parents to choose suitable teaching videos for students. However, the uniform education does not accord with the characteristics of middle school students’ physical and mental development at the present stage, and it is difficult to achieve the expected effect of teaching. This paper analyzes the characteristics of instructional videos from a professional point of view, combined with the physical and mental development characteristics of high school students to collect student evaluation of teaching video from a student point of view, to extract the students study preferences, use Collaborative filtering algorithm recommended teaching in line with students will be taught the way for students. This not only applies the convenience of online teaching, but also achieves personalized teaching services. At the same time, it also conforms to the characteristics of physical and mental development of middle school students, and greatly improves students’ enthusiasms and efficiency in learning.