Article: Research on the application of English online course recommendation model based on machine learning in college English teaching Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2025 Vol.9 No.2/3/4 pp.91 - 99 Abstract: The lack of text information, user behaviour information, and evaluation information in English online courses result in traditional recommendation algorithms not being directly applied to course recommendations. To solve this problem, based on the full analysis of NetEase cloud class user data, the study extracted four types of characteristics, namely user preference characteristics based on topics and collaborative filtering (CF), course popularity, and course instructor influence, and quantified these characteristics that affect recommendation decisions. Then, the ranking SVM algorithm was used to sort the multi class features obtained, and a multi feature network course recommendation model was constructed, which summarised the recommendation problem as a sorting problem. Finally, user interest labels are obtained using topic based preference features. Through the above operations, a machine learning (ML) based online English course recommendation model was constructed. Through experimental analysis, it can be seen that the average precision of Model 1 is 87.86%, the average recall value is 79.57%, the average RMSE value is 0.265, and the average MAE value is 0.285. The research and construction of the model can provide students with more accurate and personalised intelligent course recommendation lists when using online classrooms for learning. Inderscience Publishers - linking academia, business and industry through research

Title: Research on the application of English online course recommendation model based on machine learning in college English teaching

Authors: Qiongying Sun

Addresses: School of Foreign Languages, Chongqing College of Humanities, Science and Technology, Chongqing, 400042, China

Abstract: The lack of text information, user behaviour information, and evaluation information in English online courses result in traditional recommendation algorithms not being directly applied to course recommendations. To solve this problem, based on the full analysis of NetEase cloud class user data, the study extracted four types of characteristics, namely user preference characteristics based on topics and collaborative filtering (CF), course popularity, and course instructor influence, and quantified these characteristics that affect recommendation decisions. Then, the ranking SVM algorithm was used to sort the multi class features obtained, and a multi feature network course recommendation model was constructed, which summarised the recommendation problem as a sorting problem. Finally, user interest labels are obtained using topic based preference features. Through the above operations, a machine learning (ML) based online English course recommendation model was constructed. Through experimental analysis, it can be seen that the average precision of Model 1 is 87.86%, the average recall value is 79.57%, the average RMSE value is 0.265, and the average MAE value is 0.285. The research and construction of the model can provide students with more accurate and personalised intelligent course recommendation lists when using online classrooms for learning.

Keywords: online education; machine learning; ML; course recommendation; characteristic sorting.

DOI: 10.1504/IJCSYSE.2025.149209

International Journal of Computational Systems Engineering, 2025 Vol.9 No.2/3/4, pp.91 - 99

Received: 07 Apr 2023
Accepted: 11 Jun 2023
Published online: 20 Oct 2025 *

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Title: Research on the application of English online course recommendation model based on machine learning in college English teaching

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