Cluster Analysis of Online Learning Behavior Based on K-Means

The study utilizes a K-Means clustering analysis model to analyze the learning behavior data generated by online learners on an online learning platform. Through the use of Python for data cleaning, transformation, and determination of the number of K-means clusters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>, online learners are ultimately categorized into three types. Using the SPSS platform, this study focuses on analyzing the online learning behavior data of each type of learner, excavating typical characteristics of student learning behavior, and summarizing the learning behavior characteristics of these three types of learners. The results indicate that clustering analysis can identify different groups of learners with significant differences in group behavior characteristics, providing support for the subsequent design of personalized learning strategies.