Bibliographic Analysis of Machine Learning in Shaping Educational Psychology

Educational psychology plays a crucial role in enhancing students' learning experiences, academic performance, and personal development by ensuring their mental health. Traditionally reliant on qualitative methods such as interviews and educator assessments, this field has often struggled with the limitations of subjective and less comprehensive evaluations. Recent advancements in technology offer new possibilities for improving student psychological support. This study proposes a novel approach by utilizing bibliographic methods to investigate the integration of big data and machine learning in educational psychology. Big data encompasses extensive student-related information, including academic performance, behavioral patterns, and socio-economic backgrounds. Machine learning applies advanced algorithms to this data, enabling the identification of patterns and predictive insights into psychological conditions. By developing comprehensive databases and machine learning models, this approach facilitates the early detection of potential mental health issues such as depression, anxiety, and extreme behaviors. This proactive methodology offers timely interventions and enhances traditional practices. The use of big data and machine learning promises a more precise and data-driven strategy for managing student mental health, thereby advancing the effectiveness of educational support systems and promoting overall academic success. This study underscores the transformative potential of these technologies in revolutionizing educational psychology.