Predicting At-Risk Students in Higher Education
Keywords:
At-Risk students’ prediction, Classification, Data mining, Decision Tree, Educational Data MiningAbstract
Student performance prediction is very important and can help an educational institute to increase the success rate among students. A common problem universities face is that of students failing to complete the academic program or taking a long time to do so. Identifying at-risk students in the early stages would help to provide them with the support they need. At-risk students can be described in different ways, depending on the educational system and its requirements. In this paper, at-risk students are defined as those with a low GPA of less than 2.75 out of 5 or who have failed to graduate. The focus is on the attributes that can help recognize at-risk students in advance. The results of this study proved that at-risk students can be predicted at an early stage based on their gender, and marks on pre-admission exams, in high school, and the first semesters of their academic programs.
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