Analysis and Research on Learning Behaviors Based on K-Means Algorithm in MOOC Platforms

Massive Open Online Courses (MOOC) is a fruitful attempt for online education to target the whole society and achieve comprehensive educational equity, serving as a powerful supplement and enrichment to offline education. Learners can access courses and knowledge from all over the world through online access at any time and any place. Taking the Web log analysis of the MOOC platform as the project background, this paper utilizes the ELK technology stack (Elasticsearch + Logstash + Kibana) to complete the collection, storage, and organization of learning behavior data on the MOOC platform. Furthermore, an online learning behavior clustering analysis method based on the k-means clustering algorithm is employed, providing significant data analysis and application support for online learning platforms. This approach assists teachers and platform managers in adjusting teaching modes and strategies in a timely manner to enhance learners' online learning effectiveness.