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How Universities Can Use Data to Improve Student Learning Outcomes


Joel Salmon
Joel Salmon

Independent data analyst Joel Salmon is an experienced sales and project management professional. Currently, Joel Salmon leverages data to help higher education institutions improve their teaching services.

Many higher education institutions are using big data to provide quality learning experiences while reducing the cost of higher education. Universities and colleges already store a lot of data from student enrollment rates to course completion rates. This data can be retrieved and analyzed to streamline educational experiences, keep more students engaged, and improve learning outcomes.

Essentially, institutions use two types of data analytics. The first type is predictive analytics, which uses historical patterns to predict future occurrences. The second type is prescriptive analytics, which decides the best course of action to improve educational outcomes. Both modes are beneficial to institutions.

Predictive analytics can be used to boost student enrollment rates. School administrators can look at student demographics and related information, and then compare that data with academic records to improve candidate selection. They can also use this data to ensure scholarships go to students with higher chances of retention.

Prescriptive analytics comes into play during a student’s stay in college. School administrators can cross reference academic records with student class choices to suggest appropriate courses to lower dropout rates. They can also analyze class performances across student demographics and student attendance rates to match course instructors with appropriate classes or subjects, and to identify students who need help.