Manual processes were preventing us from building trusting relationships with adult learners at scale. With Ribbon, we can focus on deepening relationships and coaching students instead of spending hours pulling student data manually.
Reach University’s student enrollment has expanded rapidly since they launched their job-embedded undergraduate program in Fall 2020 — to the tune of 2,000% more learners in 3 years.
With 100% of their students – average age 38 – fully employed at school sites, Ko Kim, VP of candidate affairs, and the advising team at Reach University needed to find a way to scale student support without losing the warm, growth mindset-based advising model.
Ribbon’s real-time, accessible, and easy-to-understand data has given Ko and her team the tool they need to reduce manual processes and scale their effective, relational student support to ensure 70% - 80% of adult learners complete their Reach program on-time and above traditional college persistence and completion rates.
The Challenge
When Reach University expanded its job-embedded degree programs to include undergraduate offerings in Fall 2020, they had 68 enrolled undergrads. In just three and a half years, they’ve grown to over 1,600 undergraduate students, which caused scaling challenges.
One major problem we had was everything was a manual workflow prior to Ribbon, which doesn’t sound like a big deal when you have 50 or 100 students. But we’re now at 1,600+ students — it’s a big difference.
Reach University was growing, but still managing everything manually. They knew they needed to scale those manual processes — and quickly! — but it wasn’t just a question of scale. From the beginning, their manual workflow wasn’t effective enough to get their team the right data at the right time.
The advising team could not know who was really struggling until midterm grades were submitted. Unfortunately, by that point, it was usually too difficult for learners to make up work and achieve a passing grade.
We really wanted to be proactively touching base with someone and not waiting to be reached out to …. We were unintentionally doing a lot of waiting. We wouldn’t know someone was in crisis until it was too late.
Lagging data prevented the advising team from addressing critical roadblocks in a learner’s journey until it was too late. The team knew something had to change.
So ultimately, Reach’s advising team was facing two major challenges:
a manual process that couldn’t scale
a lack of real-time data that could allow them to identify, and proactively support, struggling students
The Outcomes
Despite a quickly expanding student population of working, adult learners, Reach University is having incredible success with learner retention, graduation rates, and NPS scores.
How?
Their advising team has pivoted to focus on appreciative advising with a growth mindset lens, and has implemented Ribbon Edu to get stronger data, at scale, exactly when they need it.
Having the right data, at the right time — without having to pull it all manually — has not only allowed Reach to scale their advising to a 150:1 student to advisor ratio, but allows them to support all of their students with the right level of support, garnering them industry-leading graduation rates and advising NPS scores. Reach’s emphasis on high-touch and high support experiences for all students is only possible with tools like Ribbon.
How Ribbon is Helping Reach University Focus on Relationships at Scale
With Ribbon, Reach University has implemented a proactive, relational tiered advising model with automatic, real-time data. These two solutions have worked effortlessly in tandem, leading to huge outcomes for Reach’s advising teams — and their students.
In short, they’ve found three key solutions:
Ribbon provides data at scale, eliminating manual processes
Ribbon provides the right data at the right time, for more effective intervention
With the right data, Reach has been able to implement a tiered advising model focused on growth-mindset coaching and relational or appreciative advising.
Here’s what all of that looks like in practice.
Data at scale
One of the biggest benefits Reach University found right away with Ribbon is the ability to find and analyze student data, at scale.
Instead of relying on ineffective and time-consuming manual processes to dig through student data and try to identify struggling students, Ribbon’s platform allows Reach advisors to set up flags and filters to easily understand how students are doing.
What we were doing before was, pulling reports, looking through spreadsheets, and then manually going into everyone’s Canvas, and double checking assignment grades and notes.And then maybe by chance we find the right people who need support, and we call them. But the process would take four times as long because we were just combing through manually, vs. with Ribbon, we have filters and categories. We now can quickly identify those who need tiered support right then and there.
One big marker the Reach team always looked out for was students who were missing assignments. Before, they would manually pull reports on Canvas to try to identify students who were missing assignments, but it’s difficult to do this effectively and regularly.
With Ribbon, this manual process is alleviated with custom filtering and streamlined data.
In Ribbon, Ko and her team can define how they want to filter students, so they’re getting the exact data they need. They can set up notifications and flags so that their team is notified when students are missing three or more assignments, and then easily filter by students flagged as “needing attention” to easily see who needs support and reach out to them.
The right data at the right time
The advising team at Reach knew what data they were looking for — but not being able to find it quickly, or worse, not being able to find it until midterms or later, meant that by the time they knew who was struggling, it was often too late to help.
This is another aspect where implementing Ribbon really allowed Reach to support the right students, at the right time.
With Ribbon, you get data as soon as you connect your student LMS to your Ribbon platform. You can immediately see who’s missing assignments, who needs attention, and who’s doing amazing.
From there, you can quickly filter and sort students to see who needs support at the current moment — and reach out before it’s too late.
Before, Ko says:
“We definitely missed a lot more people than we do with Ribbon. It was such a complicated workflow because, maybe you’d see someone missing one assignment, and then later missing two, so you’d be keeping tabs on them. But by that point, you’re trying to recall — did I reach out to them last week about this? Or the week before? We have spreadsheets but it was too manual and too open to user errors.”
Once they’ve identified the data they need, they can now reach out directly through Ribbon, which makes communicating and supporting students at scale frictionless - made even easier with Ribbon's AI-generated drafts.
Even better: they can now see all of those previous touchpoints right within Ribbon, so they know who needs support, who reached out last, and where the student is in their support journey.
We had 480 people at the start of the Spring 2024 semester that had been flagged as “attention needed.” By week eight, we’ve had 65% of those get back “on track.” I know for a fact that was not happening without Ribbon.
Tiered, appreciative advising with a growth mindset lens
Backed by real-time, easy-to-understand data in Ribbon, Ko’s team set about implementing a tiered advising model with a focus on a growth mindset and trusting relationships.
With a tiered advising model, the advising team at Reach makes sure students stay caught up. They can provide generalized support to students who are doing well, and differentiated, intensive advising to students who need more support.
This way, dedicated advisors can partner with all students on a consistent, frequent, and early basis — long before midterm grades hit the books. By doing so, they have the opportunity to ensure all students have the right level of support to succeed.
We want to be informed about our students – beyond their grades, missing assignments, and attendance. All of those things Ribbon does help with, but mostly, by making such data points really accessible, it allows us to focus on getting to know the student as an individual.
We get to the more deeply personal aspects of our students’ lives because Ribbon streamlines all the essential data for us. And once we get to know the candidate as a whole person, not just a number, we can truly partner with them in crossing the graduation line.
Reach University’s tiered advising model is encouraging students with a growth mindset and providing warm, relational advising that increases learners’ self-efficacy and agency.
Ribbon allows them to foster this growth-mindset coaching supported through at-a-glance, real-time data on all their students. For example, in Ribbon, they can easily sort out students who are flagged as “need attention” for additional support. But they can also quickly identify students who have brought up their grades recently and reach out to them to celebrate and recognize their success.
In both cases, advisors are leveraging a growth-mindset lens in coaching and encouraging learners’ progress. As a result, they’ve created a rich, warm, relational model where students feel supported — and ultimately learn how to give that support to themselves.
All of this adds up to create really effective results, such as super-high NPS scores (on par with Apple and Netflix) and graduation and retention rates that meet and exceed those of traditional students in 4-year universities.
Curious about scaling your own advising program?
The real-time, easily-accessible data and streamlined outreach Reach University gets from Ribbon makes their advising model and success possible at scale.
With Ribbon, Reach University has been able to:
scale their advising program as student enrollment increased 2000% in three years
eliminate manual data processes for a more scalable solution
implement a relational, appreciative advising model backed by real-time data
If your advising program could also benefit from reducing manual processes and stronger learner data to support your learners, we’re here to help. Schedule a free diagnostic today and we’ll help you determine if Ribbon will work for your program.