Discovering Learning Patterns through Data Mining in E-Learning Platforms Data Mining for Anomaly Detection in Network Traffic

Abstract

This paper explores the application of data mining techniques to discover learning patterns in e-learning platforms. With the increasing adoption of e-learning systems, vast amounts of user interaction data are generated, providing valuable insights into student behavior, engagement, and learning outcomes. This research aims to apply various data mining algorithms, including clustering, classification, and association rule mining, to analyze the data collected from e-learning platforms. The primary objective is to uncover hidden patterns related to student activity, learning progression, and performance, which can aid in the development of personalized learning paths and intervention strategies. The results of this study demonstrate that data mining can effectively identify key learning patterns, such as user engagement trends, the impact of content interactions on performance, and predictors of student success and dropout. These findings highlight the potential of data-driven approaches to enhance e-learning systems by providing actionable insights for educators and administrators to optimize learning experiences. The paper concludes by discussing the implications of these findings for improving student retention, performance, and overall educational effectiveness in online learning environments.