Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

Abstract

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.