Frontiers in Educational Research, 2021, 4(16); doi: 10.25236/FER.2021.041605.
Wenjun Li1, *, Yating Peng2
1School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102, China
2School of Information and Computer Engineering, Northeast Forestry University, Harbin, 150006, China
In the context of the new crown epidemic, online education courses have become mainstream. Along with the rapid development of the Internet and the rich reserves of educational resources, how to accurately understand the state of students to provide the required teaching courses, and how to better adapt students to online education to meet the differentiation between each other has become a mainstream problem and challenge at present. Analyze the user data, obtain the decision attributes therein, and explore the classification of online education courses; based on collaborative filtering algorithm, realize the accurate matching between students and courses. The intelligent recommendation strategy of online courses not only helps to improve the usage rate of online courses, but also helps to improve students' individuality, responding to the general policy of "stopping classes and not studying" in major universities, and carrying out more flexible teaching methods.
Internet+Education, Collaborative Filtering Algorithm, Intelligent Recommendation
Wenjun Li, Yating Peng. An Intelligent Recommendation Strategy for Online Courses Based on Collaborative Filtering Algorithm for Educational Platforms. Frontiers in Educational Research (2021) Vol. 4, Issue 16: 24-30. https://doi.org/10.25236/FER.2021.041605.
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