A single piece of data can reveal a lot. For example, whether colleges and universities are meeting their enrollment goals or comparing the number of merit based scholarships versus need based ones. But, there’s also a lot left out of those pictures, like how successful those students were in their classes or how long it took them to graduate – or if they did so at all.
While colleges and universities are eager to leverage institutional analytics, it is time for them to think bigger and broader. Doing so will open up a realm of possibilities not yet tapped, creating the opportunity to ask more complex questions and find solutions that better serve and support students.
Right now, according to the EDUCAUSE Data, Research, and Analytics unit, about 50 percent of institutions consider institutional analytics a “major institutional priority,” and 25 percent more report that it’s a major department-level priority. Given that using institutional analytics is at a nascent, but growing, stage, institutions are at a prime point to develop better data practices, so they avoid missing key signals that are hiding in disparate and siloed data.
Connecting the Institutional Analytics Dots
The key is moving from a narrow focus, looking at one piece of data from a single source, to connecting multiple data sources across campus to get a fuller picture.
A good example is academic insights. Many college and university leaders would love to use analytics to find the answer to “If a student earns a B in class, what’s the likelihood she’ll graduate on time?” But, academic insights like this one are just one piece of the overall puzzle. Combining curricular, co-curricular and student life variables – such as if that same student lives on campus, what level of financial aid is she receiving and what clubs she is a part of – will provide the visibility needed to fully understand the impact of the overall student experience on progression.
This broad view is a foundational component to build the analytics strategy institutions need.
Avoiding Costly Blind Spots
Without connecting these dots an institution is susceptible to blind spots that exist in the gaps of data. These blind spots arise when institutions don’t consider how every aspect of the student lifecycle – from first interest, to enrollment through graduation – impact the institution’s goals.
The cost of institutional blind spots is that policies, practices, and processes are put in place with good intentions but, due to lack of data, end up magnifying the problem, instead of diminishing it.
Avoiding these blind spots and using analytics more effectively is partly about understanding the types of questions institutions can ask, and where they need to look for information.
(Next page: Campus leader questions for institutional analytics 1-3)
To that end, here are three examples of broader questions institutional analytics can answer through visibility into the connections between enrollment, student success and institutional financial data.
Question #1: Are we recruiting the students that are likely to be most successful at our institution?
Why it’s a pressing question: Beyond “are we recruiting enough students,” institutions need to continue to make the tough decisions about whether they are recruiting the right students to combat the mounting pressure of meeting short term enrollment and revenue goals. By connecting student outcomes to admissions and recruitment variables, institutions can see if new recruitment strategies impact retention, for example whether an augmented financial aid policy promotes initial enrollment, but leads to lower retention among those students. Making these connections will also give recruitment teams new ways to segment and prioritize marketing, outreach, and follow up activities.
Question #2: What are my degree completion patterns, and how do we improve time to degree?
Why it’s a pressing question: Driven by the push to get more students to graduate, many institutions are looking for new ways to support student success. But, limiting data evaluation to grades or persistence rates ignores key aspects of data such as admissions profiles, tuition payment history, residential life, co-curricular participation and services received. By linking together information from across the institution and student experience, leaders will be in a better position to reevaluate the impact of student services, interventions, and incentives offered to students to keep them on track.
Question #3: How can we segment students into subpopulations to better understand and serve them?
Why it’s a pressing question: As institutions look to improve areas such as accessibility and diversity, they require a deeper level of knowledge about what different students need to succeed. As opposed to looking at the student body as a whole, institutions may potentially see improved student outcomes through segmentation to create distinct populations of students that have very different goals, circumstances and propensities to complete. Getting to that point will take seeing across an institution’s full set of data to understand which supports are most effective for which students, what commonly holds different students back, and how institutions can change their current offerings.
As higher education institutions develop their analytics maturity, they can cultivate practices to impact students’ individual experiences across their lifetime relationship with their college or university. From considering whether to apply to an institution, to enrollment, to the first day of class, through graduation, and establishing an alumni relationship with an institution, analytics can help inform and shape those experiences to be the best they can be. But only if the full breadth of institutional analytics are considered.
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