Institutions of higher education are among the best potential adopters for analytics platforms and big data methods. They oftentimes have thousands of students, and their total enrollment numbers over many decades or even centuries can sometimes number more than a million. Students also frequently take more than 40 classes to complete a single bachelor’s degree. Without getting into more granular individual data, these estimations alone represent an abundance of data to work with.
Putting this data into action, however, has required a commitment to ongoing digital and data transformations. These efforts often have been aimed at improving institutional efficiency. While this is a big target, there are several ways to hit it. Let’s take a look at how data analytics systems are transforming higher education.
What’s Being Used?
Traditionally, a good analytics package has to be backed by solid infrastructure. This means deploying database servers, oftentimes cloud-based ones, that can securely store large amounts of raw data. Likewise, these servers have to be designed with privacy and security in mind to protect sensitive student data.
Most data scientists then use a variety of solutions to prep the data for analysis. It’s not uncommon to write bespoke code for this purpose in order to correct minor issues. The data then has to be checked against to make sure nothing is:
- Place in the wrong column or row in the database
Big Data services can then be connected to analytics packages to conduct research, develop models, generate reports, and produce dashboards. From these, insights can be generated that decision-makers can utilize. Likewise, long-term data warehousing is used to maintain and share the information accumulated from these efforts.
How Can an Institution Use Data Insights?
Student retention is one of the most difficult challenges that higher education institutions face on a yearly basis. Every semester, thousands of students will decide that completing college just isn’t in reach. Frustration will eventually sabotage their academic efforts, and dropping out becomes a real risk.
Predictive and prescriptive analytics are needed to address this problem. First, predictive analytics packages allow researchers to model patterns regarding which students are most likely to have academic trouble as well as when these concerns may become critical. Once a student’s issues have been identified, prescriptive analytics will provide administrators with a list of potential solutions to apply.
Suppose a student comes from a specific geographic background that has a history of running into learning difficulties within the first two years of college. The university might have all students take certain core classes that provide a solid baseline for identifying these students. For example, a section of writing might be included as a core class to distinguish students who struggle with the basic skills needed to produce academic-quality papers.
Upon finishing the first semester of these classes, underperformers might be flagged based on the challenges that similar students have faced. A prescriptive analysis can then be used to assign them to classes or provide them with academic resources that will provide appropriate remediation to close their skills deficits. Academic support may be provided in the form of tutoring, mentoring, and other various resources.
A number of problems in higher education can be handled this way. A university, for example, might use analytics to address:
- Faculty retention rates
- The allocation of budgets and supplies
- Campus crime
- Sports team performance
- Frictions between the school and the surrounding community
- Regulatory compliance issues
What Has to be Done
Higher education is a sector that has a bit of reputation for keeping with traditional or conventional practices. However, adopting data analytics is a bit like quitting smoking: there’s is no better day to get started than today.
While institutional review processes need to be preserved, that should not stand in the way of aggressively rolling out the use of analytics. Decision-makers have to be onboarded with a data-centric culture, even if that means offering severance to folks who can’t get on board. Appropriate measures have to be taken to acquire machines, adapt existing networks, and integrate the university’s trove of data. With a long-term commitment to becoming more data-driven, an institution can achieve greater efficiency in achieving its goals and service stakeholders.