A VC Perspective - How Predictive Analytics Is Changing Higher Education

By Jay Khan on Oct 27

The age of applied sciences and predictive analytics is constructing a new path which the institutions can use to structure their learning environment. It helps them to effectively interpret institutional data and use it for being more proactive, anticipate outcomes and analyze behaviors, uncovering different usage patterns and relationships of the users.

Predictive analytics can be deployed to provide the students with countless opportunities, as it uses a number of data mining, predictive modeling and analytical techniques which can be used to project the student’s future outcomes. The institutions can also study the historical data to identify the risks and opportunities which can help shape positive outcomes while there is still time to act.

Although Higher Education is relatively a late adopter of predictive analytics as a management tool, it decided to jump right in to analyze historical and current data and determine which students are most at risk for attrition and how can they prevent it from happening.

EVC Ventures, as a Venture Capital fund, acknowledged the gap which limited institutions from recognizing the growing rate of class sizes and poor advisor to student ratio, hence making it very challenging for them to identify the students in need of help. They believe that Predictive Analytics could be a key differentiator enabling institutions to establish an analytics process with a tool to identify at-risk students at an early stage and help to shape better outcomes.

Student Success With Data & Predictive Analytics

With this in mind, EVC Ventures supported BlackBeltHelp, to assist institutions with an analytics tool which could intelligently analyze data for student success and the institution’s efficiency, creating a data-driven culture that is instrumental in achieving targets.

BlackBeltHelp Analytics focuses on the major aspects of predictive analysis to transform the higher education space using data engineering, data sciences, decision sciences and decision support.

Here’s How Predictive Analytics Is Useful

1. Increase Student Enrollment Rates - Analytics play a vital role in reducing the per enrollment cost. It assists institutions in analyzing the enrollment data and trends over a span of years by identifying the profiles of students who are likely to be a better fit for specific programs, thereby helping institutes take a focused approach and target better students.

2. Improve Student Engagement and Satisfaction Rates – Information is an invaluable tool when trying to understand how to keep your students engaged while they are learning. BlackBeltHelp helps institutions to put data to use and give students the advising support they need to succeed with the help of predictive analytics.

3. Increase Student Retention Rates - BlackBeltHelp identifies the patterns of student risk by analyzing the historical data from institutions’ student information systems. It builds predictive models to understand drivers of attrition and identify actionable triggers to help institutes reduce churn.

4. Better Financial Aid Services to Students - Business intelligence software and predictive analytics help students get more mileage out of financial aid. BlackBeltHelp provides predictive analytics with more precision, allowing the organization to stretch financial aid services out to more students in a timely manner. It also helps to predict non-payment patterns, if any, by sifting through the captured data.

5. Strategize Your IT Decision - There is an undeniable need for a systematic analysis before deciding whether an IT function should be outsourced or not. BlackBeltHelp provides a systematic analysis for evaluating components of the institution’s IT if outsourcing may be useful to practitioners. They assist in end-to-end analysis of the institutions IT department, to strategize their decisions based on their pre-existing data.

There are many success stories proving how higher education has benefited from predictive analytics, with expansion of data-driven decision-making opportunities in nearly all areas of campus life and operations. Even though so far, student retention has been the motivational factor for universities to adopt predictive analytics, insights show that it has also translated to better learning and teaching environments. Big data and predictive analytics identifies not just the current stats but also help to nudge students on the predictive findings to make better decisions and succeed.