Association Rule Mining In Student’s Dropout Risk Assessment: A Case Study
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Abstract
Student dropout risk assessment is essential for numerous intelligent systems to improve the performance and success rate of an institute. Therefore, efficient methods for prediction of the students at risk of dropping out, is the need of today’s education system which enables the adoption of proactive process to minimize the situation. This paper propose a prototype machine learning tool which can automatically recognize the causes for whether the student will continue their study or drop their study using association rule mining. It also extracts hidden information from large data about the factors that are responsible for dropout student.
In this case study, the association rule analysis is carried out to find whether the student is having a dropout risk or not so that some preventive measures can be done to avoid it and improve the performance of the student. The analysis further used to predict the students drop out risk using five major problems such as family problem, health related problem , personal problem, financial problem and institutional problem.
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