Student dropout retention and success rate prediction.

This study focuses on predicting student dropout, retention, and success rates in the education sector based on academic performance. So basically, what we mean by retention rate in the education sector is the extent to which a learner or student opts for acourse or a programme for a while. This initiative explains the student dropout rate, which is the opposite of the retention rate and results in income losses in the millions of dollars. According to current surveys and studies, a rise in the degree of retention is linked to a variety of variables, such as how a pupil is educated or the environment in which education is given that requires development. This research helps us understand the variables that contribute to these results and create a predictive model that can assist educators in intervening early and preventing student failure. Ultimately, this project aims to provide different institutions in the educational sector with a tool that can help them understand a student's performance, identify at-risk students, and find a solution before it is too late. We gathered information about the student's period in the programme in which he or she is enrolled and about their graduation ordropout, along with their socio-demographic information and programme characteristics. Ultimately, it was discovered that the numberof semesters, student scores or grade point average (GPA), country, gender, and the presence of failures or dropouts per course were the primary variables responsible for describing both the time required to complete the programme and the risk of dropping out for thesame. Additionally, we found no evidence that other factors, such as age, marital status, ethnicity, and high school education, had anyeffect on these factors (success rate and dropout rate). [ABSTRACT FROM AUTHOR]

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