Predictive Analytics in Higher Education

Predictive Analytics in Higher Education

Predictive Analytics in Higher Education

Predictive Analytics is certainly a buzzword in the technology and business arena, but what it means to higher education is different than other industries. Higher education is not looking to determine how many widgets it can sell in certain markets, but rather, will a student who enters college persist, and will he complete a degree. Gathering data to predict the success of a student can cross into personalization and also can create privacy concerns. The world is certainly moving towards personalization through data gathering. The ability to predict and increase the number of college graduates using academic, demographic, and social data must be considered. Higher education strives to achieve Amazon’s mastery of customer data gathering and use of predictive Analytics.

So what is persistence and completion? Persistence is defined as the number of students who stay in college from year to year. The biggest drop out typically happens from first to second year of college. Completion is the number of students who complete a degree. Completion and persistence rates are important because they measure how well an institution is serving its students. They also indicate the nation’s ability to fill jobs with qualified candidates.

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According to the National Student Clearinghouse Research Center (2016), the persistence rate from year one to year two is 72.1 percent. Persistence rates continue to decline during and after the second year of college. The total completion rate of students who start college and completed within six years is 54.8 percent nationally as indicated in figure 1. The persistence and completion rates are lower for part-time students. Rates of completion and persistence vary between two year and four year colleges, public and private, full-time students versus part-time students. Other demographics such as age, gender, socio-economic status, and ethnicity all play a role in these numbers as well. 

  ​Sending a student with low grades in math, a reminder about coaching or tutoring is an easy example  

The question of interest is how do colleges collect and use big data to predict and improve the success of students (persistence and completion). More precisely, how do institutions determine students who are less likely to succeed, and create interventions that increase a student’s likelihood to succeed? This becomes the tricky part as many interventions are not related to academics. Typically students are worried about finances, child care, working and attending college, and an increasing number face issues that need counseling. Because many of these areas become very personal, how and what data do institutions collect before the line of privacy is crossed.

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Higher education institutions currently collect a wealth of information on students at various points in the student’s college lifecycle from prospect to graduate. Data collected includes high school transcripts, financial data, demographic data, progress and success data, course and program data, as well as data on extracurricular activities.

 



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