How to Achieve Positive Student Outcomes by Leveraging Big Data

By Frank HealyJune 1, 2015

Student outcomes have remained a topic of significance within the higher education industry in recent years. The benchmarks for success have shifted beyond graduation to metrics such as gainful employment and student loan repayment.

Schools are being held accountable for student success, and the bottom line for students is now the bottom line for schools. Higher Ed Growth has been working to examine big data with the intent of better understanding student outcomes and the role higher education institutions play in achieving them. Let’s look at four metrics that can assist in redefining the priorities of student outcomes by leveraging big data:

1. Pre-Enrollment

Prior to student enrollment, schools can be proactive in performing a competitive analysis to discover its strengths and weaknesses compared to similar schools. In examining data, if a student is more likely to enroll when they have been matched to one competitor versus another, schools can optimize their campaigns accordingly. The Bureau of Labor Statistics also provides data that can help analyze and update current programs to better fit job demand, both nationally and regionally. This includes developing new academic programming and removing those that are no longer relevant in the workforce.   

2. Enrollment

Data transparency between partners is necessary during all points of the student lifecycle to understand success events and factors. The student lifecycle extends beyond contact and enrollment to graduation and job placement; schools should use a customer relationship management (CRM) system to track all touch points of the student journey. In exploring this data, schools may find surprising trends to help with their marketing efforts, such as a difference in enrollment rates when comparing prospects wishing to change their career versus those hoping to advance their career. This data will help both schools and partners target their efforts toward high-intent prospects.      

3. Graduation

Schools should be constantly improving their programs to increase graduation rates among their students. To do this, the student education lifecycle should be tracked via a learning management system (LMS). This data should include information from student participation, credit hours, student profiles, student success, and course quality. Schools should measure and analyze this LMS data as a tool for student outcome modeling by creating a predictive model for success events, such as graduation rates or student loan repayments. This predictive model can help in identifying patterns of success, which schools can use to adjust and improve curriculum.

4. Post Graduation

The focus on student outcomes means that schools are now looking beyond graduation to factors such as job placement and loan repayment to measure student success. This information, however, is currently not being gathered by enough institutions. Schools should focus on gathering this information through government and third-party resources. This data can also be obtained by maintaining relationships with students after graduation. Accessibility to greater data sets gives both schools and partners the ability to improve upon the student lifecycle, from enhanced marketing efforts to helping students find jobs in their desired career after graduation.

Schools and their marketing partners need to focus on defining, measuring, optimizing and predicting student success. Institutions should define what a success event looks like and gather and measure data to determine success at each stage of the student lifecycle.With that data, optimizations and enhancements can be made, and eventually, a model that predicts success at any stage of the student lifecycle. By utilizing external sources and consolidating data, schools and their partners will contribute to redefining student outcomes and the overall education infrastructure. 

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