The Continuing Education division shouldn’t be thought of as an entity that operates on its own. Instead, it should be embraced for the value, data and growth it brings to the institution. After all, as the name suggests, its mission is to help with continuing the journey of education, and that is what every school should be striving for.

The EvoLLLution (Evo): Why is data so valuable to a modern higher ed institution’s effective operation?

Jenna Cullinane Hege (JCH): Data is absolutely critical to a modern higher education institution. Big data is out in the world, and we have seen its benefits in our consumer interactions and the media. People are now accustomed to data insights and visualization throughout their life.

Modern higher education needs to be really sophisticated about where it invests human and financial capital to support students in an environment of limited resources. Data helps us think about how we are using our funding, what the best investments are and how to support as many students as possible, especially in community college education. We are aiming not only to provide an open-door policy but to bring as many students into our colleges, including low-income students, historically underrepresented students and minority students, to help them succeed as much as possible.

We need to be thoughtful about how we’re recruiting, who we’re recruiting, who we’re helping succeed and, increasingly, what kinds of supports are most effective for which populations of students. And even beyond that, how successful we are. Are many students completing? What kinds of awards are we helping to confer? What do our students do after they leave us? What are they gaining economically, socially? And what are the social benefits that extend beyond the individual?

Data is fundamental to all those areas.

Evo: What are some of the most common misconceptions that you’ve seen in higher education about what data can or can’t do?

JCH: We still operate in a domain of hunches sometimes. Checking our assumptions can be uncomfortable. It can upend the way that we have thought things work. A misconception is that we should rely too much on our hunches or the processes we have always used, but we should continue to actively use data to check ourselves.

Another misconception is that we can make do with insufficient data. There are absolutely gaps. Some pockets within higher education data are less formalized, less developed or siloed. One issue is bringing up data quality, data governance and data integration. We have many separate systems that do separate things that generate data like application systems, student information systems, learning management systems and advising systems. We need to formalize and integrate all the amazing assets we have yet to fully tap.

Evo: Why is it generally so much harder for Continuing Ed to generate, collect and analyze actionable data?

JCH: Great question. I think it comes from culture and origins. At many institutions, Continuing Education has been this thing on the side. It’s a nice, separate, independent, autonomously running unit that really exists away from the core academic credit operations.

As community college leaders, when you look and reflect on your institution, is CE completely integrated? Is it perceived as equal? And is the data as formalized as on the credit side? Contributing to that culture are regulations that require credit to be more formalized. We have more reporting requirements on the credit side, so the mechanisms for data collection and reporting have developed out of a need.

CE has lived in a space with much more flexibility and much looser state oversight and reporting requirements. That contributes to this culture of division.

Historically, CE was more focused on business operations. The credit side was always based out of our student information system and Enterprise Resource Planning (ERP) system. The credit side is more affiliated with our Institutional Research division and state reporting rules. The CE side was managed out of the business office with an emphasis on financial sustainability: how many students, how many classes, how much tuition, how much revenue. It was much more transactional in that we might offer a two-hour CE class on one day, and that’s all that is for a particular student. Operational success was defined by filling classes and revenues covering costs. So, they just came up with a different perspective on why they needed to keep track of data. It was a little bit more of a business orientation than a formal accountability or academic rigor orientation.

Evo: What’s the ideal data infrastructure, taking the best of Continuing Ed, while bringing in the best of the main campus?

JCH: The credit side is more formal, tends to have better data quality and more comprehensive data collection. We have been on a mission to mature our CE data resources to be on par with our credit. Leveraging the best of what CE has to offer is more about programming and customer service.

One strengths are flexibility and innovation, and they are responsive to student and employer needs when they come up. If 500 firefighters need a Continuing Education opportunity, and they need it next month, we can do that. So, they’re really nimble, and they have really good ties to the community they’re serving. And those are things the credit side would benefit from thinking about.

Evo: We’ve talked about the data gap in Continuing Ed specifically, but how can that data gap impact the modern Continuing Ed’s operations over the long term?

JCH: It’s critical. We have recently emphasized building relationships between our Institutional Research department and our Continuing Education division. It was like we were not speaking the same language at first. CE folks saw having more data requirements as more compliance and maybe a headache. But now that we’ve improved data accuracy and comprehensiveness, they say, “Oh, look at all these great reports. We can understand what’s happening with our students so much better.”

We have always had data on students in CE courses, that’s what we’ve always had, but we did not have data on students in a program of study or major. It’s important to have program data if you want to understand the student lifecycle and the experiences that build to successful completion. It’s not enough to know you have 45 students in flower arrangement classes and 100 in fire science.

Typically, students are trying to build a set of skills. Getting beyond simple course counts meant we had to formalize programs, and we need to know which programs each of those courses are affiliated with. If you want to have a completer, you have to know what courses are in that program and what the last course is in the program. Suddenly, we can grant people an award for an accumulation of courses. We formalized both what we call institutional awards and occupational skills awards, which are often below the number of contact hours required for reporting to the state. Actually, in the state of Texas, it’s optional to report these short-term non-credit awards. Austin Community College District does because we want to, for ourselves and for the state, acknowledge the contributions we’re making to the labor market.

As an aside, there is growing interest among state and federal governments, higher education foundations and intermediaries in bolstering short-term award reporting. I suspect we will see a stronger emphasis on reporting and CE data collection in the next few years.

We can now track our CE (and credit) students consistently, even after they leave our college. The first big leap was to start tracking program completions and granting awards, so we can assess how successful we were in moving students through to a goal. The next big leap was connecting our completers to their wage records. We historically relied on either general labor market information or anecdotes from individual employers. We might work with a healthcare provider who says, “Oh yeah, we’re paying $17 an hour.” But when we match our students to the wage data, we find that students aren’t making as much or maybe they’re making more. As an institution, we really needed to evolve to get to a level where we had a more systematic view on post-completion outcomes for each of the CE programs we offer.

The net result of many of these kinds of data infrastructure, data quality and data alignment efforts is that we can include continuing education in all our big strategic planning efforts and routine internal reporting like daily registration updates. When the data is of high quality, we don’t just say, “Oh, we had 40,000 credit students in the fall.” We had 40,000 credit students plus 10,000 continuing education students, and we can talk about them consistently instead of always having these two separate stories. That was big for us.

Evo: How does that wind up informing investment and growth strategies?

JCH: It’s been a big deal for us since the start of the pandemic. And it’s part of a big national conversation around short-term programs, awarding Pell grants for short-term programs [CA1] and rapid reemployment efforts. Austin Community College’s approach was to identify a set of 27 six-month CE programs that could be done partially or fully online. We wanted to offer these 27 identified programs at a 50% tuition discount. We really needed to be responsive to the community. Lots of folks had employment disruptions because of the pandemic.

We decided to make these programs a big institutional priority. And as a result, it puts more pressure on the data. If we’re going to offer a 50% tuition discount, we want to understand how it affects enrollment. And we want to understand how successful we are with those students. We want to know who’s a completer and what the employment rates are.

Evo: Is there anything you’d like to add about creating a culture of data analysis in the Continuing Ed space?

JCH: I met with my CE folks just to prep for this conversation because I was really surprised how many folks have said, “Wait, those employment dashboards are nice and all, but how the heck do you even have the data to do any of this?”

I went back to my folks and asked, “How did the institution get to this level of data maturity? It feels like we’re ahead of the game.” I know the activities we’ve been doing for the last two years, since I’ve been in my position. Basically, what I came to find out is that this has been a 20-year maturation. Our CE group wasn’t originally in our student information system. They were on their own separate system. Over time, CE moved to the same student information data system as our credit programs, which maybe they didn’t like at first, but ended up being really positive. And then we had to address the separate application systems and later our learning management system.

When it came to systems, software and data, CE tended to trail. But when we brought them in, it just helped bring up data quality. And it’s more data coverage at that point.