[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Abstract

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

Research and Development of User Clustering-Based Content Similarity Algorithms in Dance-Assisted Choreography Techniques

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COVID-19 Influenced Survey on Students' Satisfaction With Psychological Acceptance Based on the Organization of Online Teaching and Learning in English

“…In the context of the pandemic, universities should provide ongoing support in various forms to assist academic staff in their online journeys. There is currently a growing gap between managers' and faculty's attitudes toward online education, as online education has always been controversial because of the virtual nature of the network (Baber, 2020 ; Gupta and Chaudhary, 2020 ; Zaher et al, 2020 ; Fang and Li, 2021 ).…”

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

“…However, the computational efficiency of this algorithm will decrease significantly with the increase in data volume, and it cannot be well applied to the case of sparse data. Reference [22] studied and analyzed the English course recommendation technology by combining the bee colony algorithm and the neural network algorithm.…”