The National Center for Education Statistics estimates that forty-five percent of students obtain a degree or certificate at the first institution they attend within six years of starting college, and nearly 31 percent drop out entirely. Those statistics demonstrate why student success has never been more important in higher education.
Every student's journey to graduation is unique, and to keep a student on that path, institutions need to know what's working. Enter data and analytics for student success.
Educause notes, "Retention and graduation are the hallmarks of student success metrics. However, if we are measuring only retention and graduation, we lose the opportunity to learn if what we are doing to support student success is working and to make any needed course corrections. Retention and graduation are lagging indicators."
Instead, if a college focuses on leading indicators, they may identify what's causing students not to retain or graduate.
That's why data and analytics are essential to improve student success on a campus. By identifying leading indicators, colleges can take a proactive and data-driven approach to student success.
By the end of this post, you'll know the steps for getting started with data and analytics at your institution.
Keep reading below to get started!
As we've worked with institutions on student success initiatives, we've identified a few key steps that all colleges and universities can use to get started on the path to find those leading indicators.
What specific student population do you want to focus on for retention? Is it undergraduate? Graduate? Online?
Some institutions already know the population they want to support. It may be a specific class retention (i.e., sophomore to junior) or related to a specific program or major. The population may be under-represented students or first-generation students. Even if you don't know the population to target, data and analytics can help determine which students need the most focus and support.
Once you have identified the who, then the data collection and organization begins. The data may be spread out among multiple systems at your school, like the CRM or the SIS. You'll want to collect student-level data, including:
You'll also want to gather data about interventions such as advising appointments, tutoring sessions, mentorship programs, and others specific to your school. Data from swipe information can also help understand how students are taking advantage of all your campus has to offer, no matter if they are residents, commuters, or virtual.
These data 'inputs' will prepare your institution for the next step, the 'outputs' of the predictive models.
The outputs are the predictive models that help guide and inform your strategic decisions and provide automated recommendations, aka prescriptions. The models take the data and make sense of it for you.
Models can be built for the distinct phases in your academic year. You'll want to build models that can be updated as much as daily to provide up to date predictions and likelihood scores. That's important because each student's behavior can change the model and the predictions.
You'll want to ensure your model provides likelihood scores for each student, such as "What is the likelihood that the student will retain?" Looking at retention at the student level and the aggregate gives you the best opportunity to take the best actions for your students and meet your goals.
One of our partners, a regional state university, followed the steps above and gained insights into their institutional retention practices.
Are course credits an indicator of student success?
Insights showed that the number of credits a student registered for the spring term was a top importance for retention at the college. Students who registered for under 11-credits were less likely to retain to the fall semester than those with a larger course load.
Knowing that course credits are an indicator of student success, the university changed its messaging around course registration.
When reminding students to register for classes, they included messages about getting help with registration so a student could get the class or lab they needed to get above the 11-credit threshold.
Who are the 'middle students'?
The institution used data and analytics to look at retention rates across its six academic colleges and see the number of students likely to be retained.
The students were grouped by likelihood scores. Students in the 50 to 80 percent grouping were called the middle students, and campaigns were built to increase their likelihood scores.
The academic college deans, advisors, department chairs, and the Office of Student Affairs all used the middle student list to build support and momentum for the campaigns and provide an additional layer of outreach.
For this university, data and analytics resulted in more targeted and intentional campaigns. Resources and time are valuable assets, and the insights they now had helped the university be action-oriented by focusing on the students who needed intervention the most.
Colleges and universities recognize that student success is more than just retention and graduation rates. They use data and analytics to better understand their students, identify indicators of retention, and build policies, interventions, and campaigns that are effective, timely, and intentional. Data and analytics for student success could be the differentiator that helps institutions retain students and create engaged and successful alumni.
Do you know the indicators of retention at your institutions? Contact us at firstname.lastname@example.org to schedule a discussion.