Ran Cui. Application of K-Means Clustering Algorithm in Online English Learning Effect Evaluation. Dynamic Systems and Applications 29 (2020) No. 5, 1931 – 1939

https://doi.org/10.46719/dsa202029526

ABSTRACT.
The ability to understand, inventory implicitly learn and manage online, and enable them to deliver that information to those who need it when it needs to be achievable today. Creating an organizational culture of online learning is important. In the learning category, anything that has a clear online learning can be seen clearly, making it easy to get and save. The use of new technologies in online learning creates a pleasantly useful assessment of the learning environment. It has a greater impact on analytical performance and learning achievement. It should encourage learners to use a variety of authentic online resources that enhance curiosity, intensity and learning style of the English.  The purpose of the proposed computer system analysis log file is to provide information to learn English online and to identify any inconsistencies that may not be perceived by the user, so that inconsistencies can be fixed with this K-means clustering before an issue escalates. In this proposed system using the K-Means clustering algorithm based on the online English learning management system, the user maintains log files to find out who spends the most time learning online.  K-Means Clustering offers the opportunity to streamline a large scale scattered online learning, laying the foundation for meaningful clusters and smooth descriptive browsing and navigation systems.

Keywords: Online English Learning, K-Means Clustering, Log Files