Rajamangala University of Technology Thanyaburi Dropout Dataset (RDD)

Thailand's national development relies on higher education, posing challenges for the government to enhance graduate competence. High dropout rates impact education quality and student welfare, necessitating a comprehensive study. This research collects a dataset on student dropout and utilizes classification models to predict dropout likelihood at Rajamangala University of Technology Thanyaburi (RMUTT), Thailand. The dataset includes 2,137 undergraduate students from 2013 to 2019 and follows the CRISP-DM model, utilizing internal data sources from ARIT. Leveraging this dataset helps understand the challenges universities face in maintaining student quality and well-being, contributing to effective strategies for improving higher education in Thailand. The dataset analysis identifies influential features in the General Education Course, English for Communication, related to dropout cases. Future research aims to develop a predictive model that utilizes these features to accurately determine dropout likelihood. Upon enrollment, new students' individual features will be input into the model, providing personalized advice on whether to take the course in the current semester or delay it to a later semester, considering its flexible timing. This guidance aims to reduce the dropout rate based on the model's predictions. Overall, this research addresses challenges related to student quality and well-being, enhancing the overall quality of higher education in Thailand.