Abstract
The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate
Highlights
Education has been an important factor for developing a country as it produces skill and educated labours who in turn are the key factor for successful economic development
The prediction with high accuracy in students’ dropout is beneficial as it helps to identify the students at the risk stage of academic performance
We investigated the student records of Faculty of Science, Prince of Songkla University, Thailand such as admission method, major, education status, study term, grade point average, students’ high school province, and grade point average at the students’ high school
Summary
Education has been an important factor for developing a country as it produces skill and educated labours who in turn are the key factor for successful economic development. One study showed that some students could not pass through the university level [1]. This issue is affecting the academic field and influences the image of the country. Finding hidden patterns or prediction trend in vast database helps to improve the quality of management decision-making which can allocate resources appropriately with a better understanding of student learning environment. The prediction with high accuracy in students’ dropout is beneficial as it helps to identify the students at the risk stage of academic performance. Data mining has been shown the successful benefit in the business domain and it can be a suitable tool to benefit in the educational domain for finding useful information hidden in the huge dataset
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