Enhancing Student Performance through PTM and Attendance Monitoring Using a Machine Learning Approach

This study explores the application of machine learning models to predict student attendance and Parent-Teacher Meeting (PTM) participation, with a focus on factors such as academic performance, socio-economic background, and historical attendance patterns. The aim is to develop predictive models that can assist educational institutions in optimizing attendance management and increasing parental involvement in PTMs. By utilizing supervised learning techniques, the study trains models on student data to predict attendance trends and PTM participation, followed by evaluating the models on test sets to assess their performance. The results highlight the potential of machine learning to identify at-risk students, predict absenteeism, and enhance PTM scheduling, offering actionable insights that can improve student engagement and parent-teacher relationships. The findings contribute to the theoretical understanding of data-driven approaches in education and provide practical recommendations for educational institutions seeking to improve attendance management and parental involvement. Future research could further improve these models by incorporating additional factors such as student