Higher education institutions should school themselves on the best ways to use their data.
Higher education institutions are drowning in data from website usage, email and social networks, among other sources. This is in addition to student data, including information about assignments, activities and performance. But smart colleges and universities are not letting the waves of data overwhelm them. Their leaders are applying tools and proven practices for business intelligence, predictive analytics, financial performance and strategy management – all with an eye toward increasing efficiencies and improving student success.
Two of the most powerful data analytic tools in education are predictive and prescriptive analytics. Predictive tools answer an important question: Based on what’s already happened, what’s going to happen next? Prescriptive answers the ensuing question: In light of what we believe is going to happen, what are recommendations on how to best respond? These two dimensions of smarter analytics allow educational decision-makers to detect patterns that exist within the masses of data, project potential outcomes and make intelligent decisions based on those projections.
Below are seven ways smart colleges and universities can use data and analytics:
1. Target Student Scholarships
Instead of pushing out scholarships to students who might be prospects and hoping they attend the school and succeed, higher education institutions can use data analytics to attract higher quality students who have a greater chance of staying at the college or university. This provides the institution with the greatest return on investment for its scholarship dollars.
2. Improve Admissions ROI
By running a set of equations weighing demographics, academic history and other factors for different student populations (freshmen, transfers and so forth) against potential prospects, higher education institutions can target students who have the highest probability of attending. This results in the highest recruitment yields within each category of student type.
3. Identify At-Risk Students
Institutions can go so far as to identify at-risk students before class even begins by cross referencing a student’s past performance with classes for which they have enrolled or registered. Once identified, advisors can take steps to amend the student’s schedule and put him or her in classes that would prepare the student for more difficult coursework.
4. Track Attendance
Higher education institutions can keep track of student attendance by integrating attendance data taken at the beginning of class with course-related information from a student information system. Those attendance records can be compared to predefined limits personalized for each student and notifications can be generated whenever class attendance falls below the specified threshold. Educators can be sent alerts in reports or on a dashboard, which allows them to intervene and find out what’s standing in the way of the student’s success.
5. Evaluate Curriculum
Data analytics can also be applied to another piece of the retention puzzle: curriculum evaluation. Administrators can be alerted when a specific course is experiencing larger-than-normal dropouts, allowing them to investigate the cause and identify whether it’s a problem with the standard of teaching, the lecturer or something else.
6. Identify Key Donors and Investors
In the area of fundraising, higher education institutions can use analytics to go through their databases of alumni and identify those individuals with the greatest propensity for giving donations – and with the greatest potential to increase their donations as their careers evolve. Likewise, predictive analytics can be applied to uncover those donors who may be disengaging from the institution and becoming less likely to contribute. Prescriptive analytics can offer ways to keep them actively engaged. The result: more donors giving more money over longer periods of time.
7. Save on Operations
Data analytics can also be used to save operating costs and cut energy use. By using real-time data from sensors, actuators and meters, and dynamic-pricing data to assess, track, forecast, simulate and optimize energy consumption, education institutions can develop performance models. Additionally, they can identify underperforming buildings and determine the causes of energy inefficiencies.