INTELLIGENT MONITORING OF STUDENT ACTIVITY IN PHP-BASED E-LEARNING SYSTEMS

Abstract

The widespread adoption of e-learning systems has significantly increased the volume of data related to student activity, including login behavior, content interaction, and learning engagement patterns. While such data provide valuable insights into learning processes, many PHP-based e-learning systems lack intelligent monitoring mechanisms capable of analyzing student activity in real time. As a result, academic supervision often relies on manual observation or static activity reports, which limits the effectiveness of timely interventions. This article investigates the design and implementation of intelligent monitoring mechanisms for student activity in PHP-based e-learning systems. The proposed approach integrates artificial intelligence techniques into a traditional PHP platform to enable automated tracking, analysis, and interpretation of student activity data. By leveraging AI-driven monitoring, the system aims to identify abnormal behavior patterns, detect disengagement, and support proactive academic supervision. The results demonstrate that intelligent monitoring enhances the visibility of student activity dynamics and enables early identification of potential learning risks. The proposed framework shows that artificial intelligence can be effectively integrated into PHP-based e-learning environments to support continuous monitoring without requiring extensive system restructuring. This research contributes to the development of intelligent e-learning systems by providing a practical model for AI-supported student activity monitoring in widely used web-based platforms.