Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

Abstract

With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.