Bibliometric Analysis Impact of Machine Learning on Mental Health in Student Learning - Fadhil Muhammad Basysyar; Dadang Sudrajat; Gifthera Dwilestari (Prosiding SISFOTEK, 2024)
The integration of machine learning in educational settings offers promising avenues for addressing mental health challenges among students [1]. This study conducts a bibliometric analysis to explore the impact of machine learning on mental health within student learning environments. By systematically reviewing peer-reviewed articles, conference papers, and relevant literature from the past decade, this research identifies key trends, challenges, and opportunities in this emerging field. The study focuses on the effectiveness of different machine learning methodologies in detecting, diagnosing, and intervening in mental health issues, highlighting the potential for early identification and personalized support. Furthermore, it addresses critical concerns related to data privacy, ethical considerations, and algorithmic biases, which are paramount for the responsible deployment of these technologies. The findings reveal significant advancements in the application of natural language processing and wearable technology data for mental health monitoring. However, gaps remain in longitudinal studies and the consideration of cultural and contextual factors. This research contributes to the existing body of knowledge by providing a comprehensive overview and identifying directions for future research, ultimately aiming to enhance the well-being and academic performance of students through innovative machine learning solutions.