Application of K-means Clustering Algorithm and RFM Model for Student Segmentation

In the rapidly evolving domain of online education, understanding student behavior is crucial for enhancing learning experiences and outcomes. This study examines the application of the K-means clustering algorithm alongside the Recency, Frequency, and Marksum (RFM) model to effectively segment students on an online educational platform. By analyzing data spanning from December 2009 to December 2011, we aim to identify distinct groups of students based on their interaction and performance metrics. This segmentation allows for targeted interventions designed to improve student engagement and retention. The dataset, comprising over a million records, includes variables such as exam codes, question descriptions, marks, exam dates, and student identifiers. Through detailed data preprocessing, including handling missing values and converting data types, we ensure the dataset's integrity. The RFM model is employed to calculate scores reflecting students' recent activity, frequency of tests taken, and total marks achieved. By applying K-means clustering, we categorize students into meaningful segments, enabling tailored strategies to boost participation and academic success. This approach not only enhances the personalization of educational content but also contributes to the broader goal of optimizing online learning environments for diverse student populations.