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

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 wi...

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Autor principal:	Xinyao Yang
Formato:	article
Lenguaje:	EN
Publicado:	Kassel University Press 2021
Materias:	personalized demand teaching video automatic recommendation recommenda-tion system video management interest degree Education L Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/6455dc15b6804e91a9ab8975be6dca16
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spelling	oai:doaj.org-article:6455dc15b6804e91a9ab8975be6dca162021-12-02T18:42:58ZAutomatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands1863-038310.3991/ijet.v16i21.26861https://doaj.org/article/6455dc15b6804e91a9ab8975be6dca162021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-jet/article/view/26861https://doaj.org/toc/1863-0383With 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.Xinyao YangKassel University Pressarticlepersonalized demandteaching videoautomatic recommendationrecommenda-tion systemvideo managementinterest degreeEducationLInformation technologyT58.5-58.64ENInternational Journal of Emerging Technologies in Learning (iJET), Vol 16, Iss 21, Pp 42-57 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	personalized demand teaching video automatic recommendation recommenda-tion system video management interest degree Education L Information technology T58.5-58.64
spellingShingle	personalized demand teaching video automatic recommendation recommenda-tion system video management interest degree Education L Information technology T58.5-58.64 Xinyao Yang Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
description	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.
format	article
author	Xinyao Yang
author_facet	Xinyao Yang
author_sort	Xinyao Yang
title	Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
title_short	Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
title_full	Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
title_fullStr	Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
title_full_unstemmed	Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands
title_sort	automatic recommendation system of college english teaching videos based on students’ personalized demands
publisher	Kassel University Press
publishDate	2021
url	https://doaj.org/article/6455dc15b6804e91a9ab8975be6dca16
work_keys_str_mv	AT xinyaoyang automaticrecommendationsystemofcollegeenglishteachingvideosbasedonstudentspersonalizeddemands
_version_	1718377742112653312

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

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