NSSC Followup: 3 Keys to Improving Student Success Through Data EfficiencyReading time: 5 minutes
Recently, Rapid Insight attended the 2020 National Student Success Conference (NSSC) in Tampa, and we wanted to share a few takeaways about our experience there. In short, what a refreshing take on student success! The range of sessions on offer epitomized a holistic, comprehensive approach to student success. The keynote’s focus on setting students up for a successful life, rather than just a successful graduation, really set the tone for the entire event.
The conference featured discussions of student orientation, chatbots, peer mentorship, emergency aid, and everything in between. The expertise and creativity behind the attendees’ efforts to lead students to success was very motivating.
As higher education analytics specialists, we reflect after each conference we attend, seeking ways to contribute and bring value to the higher education space. At the NSSC, a common theme emerged around the value of subject matter expertise and the importance of comprehensivity student success initiatives. Our key takeaway is that streamlining your work with data will enable more timely, nuanced discussions about student success on your campus, equipping experts to do their best work.
Here are a few examples of how data efficiency can help strengthen initiatives on your campus and lead to better outcomes for your students.
A Holistic View of Student Success Data
The NSSC conference was full of student success innovators leading the charge of recognizing modern post-secondary student success is a holistic challenge. Student success should be addressed through cross-departmental collaboration with a multi-faceted goal in mind, rather than one focused only on grade point average and test scores.
We discussed this topic in a recent webinar called “Using Holistic Data to Craft a Student Success Strategy”. Modern university campuses offer students an enriching, immersive, multi-dimensional experience. Paradoxically, the metrics used to quantify student success are often still limited to grades and graduation rates.
The contributing factors to academic success or failure include a student’s level of campus engagement, health and wellness, family involvement, and many others. So why aren’t these factors being tracked, measured, and incorporated into larger student success strategies?
In some cases, the answer is that University staff simply don’t have the tools or understanding of how to incorporate these factors into student success strategies. That is where Rapid Insight can play a critical supporting role. Institutional subject matter experts and student success specialists can take advantage of our intuitive, easy to use data tools and free, unlimited training and support to gain a true, holistic understanding of what contributes to student success.
With this understanding, they can form truly comprehensive strategies to assist students not just in graduating, but in succeeding in life.
“Success Coaching” for At-Risk Students
No software can rival the value of a subject matter expert at an institution. However, when experts are able to pair their skillset with the right tools, it often leads to remarkable results. Considerable benefits come from equipping advisors and administrators with tailored datasets and predictive modeling results to further their work in student success.
An excellent example of this idea in practice comes from the work of Jennifer Green, the Associate Vice President for Enrollment Management and Student Success, and Emily Heady, the Senior Director for Student Success, both of Longwood University.
As a University on the smaller side, Longwood, which accepts around 1,000 new students each year, has the capacity to match at-risk students with “success coaches”. These coaches are members of the faculty and staff who lend additional attention and support to students who risk dropping out or falling behind in their studies.
Predictive modeling allowed Green and Heady to vastly improve their ability to identify and flag at-risk students based on factors that contribute to risk of attrition. This can be done on an individualized basis, so success coaches have a list of students to proactively monitor. The data points that feed into their model include high school GPA, enrollment in DFW courses, and test scores. This gives success coaches an early idea of who may need extra support as the school year goes on.
As the program develops, Green and Heady continue to bring additional factors into their reporting, such as course performance, scheduling, and deposit date information. The more variables they incorporate into the model, the more comprehensive their understanding becomes of what contributes to student success.
If you’re interested in learning more about Green and Heady’s work, watch our recorded webinar here!
“Nodes” of Data Usage on Campus
Supplementing your experts with the data they need, such as calculated probabilities of retention, completion, gainful employment, or any number of outcomes, is where the impact of data analytics soars. Quick and easy analytics, regardless of your background in data, is a monumental asset to your institution.
A shining example is Lipscomb University. In 2011, Matt Rehbein was hired as the sole member of the Institutional Research Department. Rehbein was faced with the monumental task of serving the entire University’s varied data needs.
Rehbein worked tirelessly to serve Lipscomb’s many departments. The constant influx of reporting requests meant most of Rehbein’s bandwith was consumed with compiling and sending these reports rather than diving deeper into the investigations he wanted to undergo.
Rehbein’s solution was to establish what he calls “nodes of Institutional Research” across Lipscomb’s campus. Essentially, he made data investigations a participatory activity. He equipped each department not just with the answers they sought, but with the training and capability to use tools to ask questions and get answers from the data on their own.
Those tools were Rapid Insight’s Construct, Predict, and Bridge. Rehbein uses Construct and Predict to organize the campus’s many data sources and build predictive models. The data is then shared with users over the cloud through Bridge, an easy to learn visualization and reporting tool.
With access to live data, experts and stakeholders across Lipscomb’s campus build their own reports and dashboards with just a few clicks. This equips them to make more informed decisions on important initiatives ranging from Enrollment Management to Student Success, and enables Matt to spend more time unearthing deep insights.
To read about the full impact of data analytics at Lipscomb, read our Case Study.
We’re already looking forward to next year’s NSSC conference, but between now and then, please reach out if you feel we can help solve some of your data troubles.
Until next year,