Analysis of factors affecting middle school student achi-evement using data science and machine learning methods

This study aims to identify and analyze the factors affecting middle school student achievement using data science and machine learning methods. Emphasizing the impor-tance of a data-driven approach in education, this study aims to contribute to the unders-tanding of the complex factors influencing student achievement and the design of more effective educational policies. The primary focus of the study is to determine the factors affecting the success of middle school students. These factors include student characteris-tics, family factors, school environment, and teacher factors. Identifying these factors is crucial for pinpointing weaknesses in the education system and implementing personalized interventions to improve student achievement. This study was conducted to determine the factors affecting student achievement levels in middle schools in Batman-Merkez. As part of the study, students were asked to complete a form to evaluate factors that could affect their achievement levels. The responses obtained from these forms were combined with the students' term averages obtained from the relevant schools. These data were analyzed using data science and machine learning methods. Data science and machine learning methods offer powerful tools for analyzing large datasets and identifying factors that af-fect student achievement. Various graphical analyses, regression models, and classifica-tion methods were used to identify and predict the factors influencing student achieve-ment. By predicting students' grade point averages, their weaknesses were identified, and personalized solutions were provided. The results of this study can help educators, admi-nistrative units in education, parents, and students develop more effective strategies to improve student achievement at the middle school level. Furthermore, by demonstrating how innovative methods such as data science and machine learning can be used in educa-tional research, it can contribute to future studies in this field.