Higher education institutions are facing increasing pressure regarding student retention as many rely on government funding that is based on how many students the institution graduates, not how many it enrolls. The cost of recruiting and educating students is continuing to rise as well, which makes student retention very crucial to the bottom line. Using machine learning algorithms will reduce the risk of attention by predicting when a student is likely to leave, assessing the cause, and triggering appropriate interventions to encourage students to stay. The goal is to predict when students are likely to drop out and giving them the right help at the right time before they would drop out.
We would use a variety of historical data on student demographics, academic results, and attrition in machine learning algorithms to identify key variables that correlate with attrition. The machine learning models use the key variables to assess current students based on their demographic data and academic results at entry and after every course assessment. There are also risk scores assigned to each student and they are classified into a risk category that faculty are alerted to so they can take appropriate action to help students who are at a high risk score.
Higher education institutions that have been using predictive analytics for identifying high risk students before the school term begins are able to take a proactive stance in designing intervention plans for potential at-risk students. Cutting student attrition benefits both the student and the institutions. Students are able to achieve their academic and career goals, and the institution mitigates the financial losses associated with student attrition, which in turn allows the institution to invest further in student success.