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.

Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

“…The interest degree of data is calculated according to the characteristics of the content. Based on collaborative filtering recommendation algorithm and hybrid sdae recommendation model, the interest of college mental health teaching materials is calculated [26]. Assuming that the total number of times students browse a certain material isa, the length of a single browsing ist, the total number of bytes isB.…”

Approach for Eliciting Learners' Preferences in Moocs Through Collaborative Filtering

“…This way, you can get rid of most of the disadvantages of "unmixed" systems. For example, in online clothing stores, recommendations show items that are similar to those you have already viewed, as well as those purchased by users with similar tastes -that is, both content-based filtering and collaborative filtering are enabled at the same time [17].…”

English teaching system design based on ID3 algorithm and node optimization

“…Concomitantly, the predicament of “resource overload” incessantly vexes educational institutions, thereby engendering an exigency for the expeditious implementation of a cutting-edge intelligent recommendation algorithm. This algorithm shall serve the purpose of tailor-made recommendations for distinctive English teaching resources (ETRs) predicated upon the proclivities and idiosyncrasies of English learners, as well as pertinent circumstantial factors, thereby culminating in an amplified efficacy ( Gao, Xing & Yin, 2021 ; Yang, 2021 ).…”