A personalized recommendation system for teaching resources in sports using fuzzy C-means clustering technique

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education insti...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 28; no. 1; pp. 703 - 720
Main Authors: Chen, Jiayong, Zhou, Guangzhen, Zhong, Yize
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2024
Subjects:
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Personalized teaching
Fuzzy clustering algorithm
Data mining
Recommendation of teaching resources
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C -means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09421-0