Systematic Review on Real-Time Students Behavior Monitoring using Machine Learning

Student behavior analysis is an important research area that aims to understand and improve student outcomes. Machine learning has emerged as a powerful tool for analyzing student behavior, allowing researchers to identify important patterns and predict future behavior. Further, student behavior analysis can help to identify individual student needs and preferences, allowing for personalized learning experiences that are tailored to the unique needs of each student. This can improve engagement and motivation, and help students reach their full potential. This study provides an overview of various methods for student behavior analysis using machine learning, including clustering, classification, time-series analysis, recommender systems, and natural language processing. Moreover, steps involved in using these methods, including data collection, preprocessing, feature engineering, model training, and model evaluation is elaborated. Furthermore, discussion on the architecture, and ethical considerations for using machine learning in student behavior analysis depicted. Finally, the article highlights the importance of carefully choosing the appropriate method for each research question and considering the potential impact of machine learning on students and society.