In the age of numbers, don’t lose sight of qualitative data

Knowing that a student is first-generation, low-income isn’t the same as sitting at the kitchen table with them when they say, "I can't do it anymore because I can’t find childcare. I have to withdraw."
Jason Levin
Jason Levin
Jason Levin is the executive director of WGU Labs, an edtech incubation, research and design arm of Western Governors University. He served as Vice President of Institutional Research at WGU for over seven years.

Data is integral to higher education. Data plays a pivotal role in advancing solutions to complex issues such as enrollment and affordability, as well as improving decision-making, student success and diversity. As colleges and universities continue to develop into ultra-intelligent institutions, data will be a requisite for navigating change and improving student learning experiences.

As a numbers person myself, it’s an exciting time. As a former vice president of institutional research, I’ve come to realize that making decisions based on quantitative data alone paints an incomplete and often biased picture. The focus is often too oriented on what is or can be measured and not necessarily on what matters.

We simply can’t “numbers our way” into understanding students’ unique challenges, especially those from under-resourced communities. As leaders make investments to gather, analyze and act on institutional information, part of the data set has to be qualitative.

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Understanding the ‘why’ by asking users directly

My team and I employ user interviews, co-design sessions and open-ended surveys to amplify important stakeholder voices such as learners and faculty members, in turn helping our researchers better understand the “why.”

Racial disparities in education outcomes are a prominent example. Qualitative data enables institutions to go beyond national trends and headlines around  standardized test performance and college graduation rates to understand the experiences of their own students. Recently, my colleagues and I at WGU Labs worked with an online institution that saw differences in student outcomes by race. We had the hard numbers to show the patterns and wanted to understand the story behind them using surveys and interviews. Our findings demonstrated how complicated the picture was.

We found that while students from communities of color reported that race and ethnicity are important aspects of their personal identity and student experience, faculty members at the institution received little information about these elements of their students’ identities. Another factor was that many white faculty members took a “colorblind” approach to advising and guiding students.

Though well-meaning, faculty unintentionally dismissed vital aspects that many students said were essential components of their identities.

Weaving together threads of data

Qualitative data provides context to quantitative data, enabling researchers and learning experience designers to create personalized, agile solutions that better support students. By bringing together multiple types of data, we can interrogate purely numerical institutional findings and determine their limitations and potential biases.

For example, colleges and universities spend a lot of time estimating stopouts with sophisticated models that rely on factors such as high school GPAs, credit hours, working status and whether the student’s parents attended college. But we still don’t really understand why people stop out. Those data points are correlates, not push factors. For example, knowing that a student is first-generation, low-income isn’t the same as sitting at the kitchen table with them when they say, “I can’t do it anymore because I can’t find childcare. I have to withdraw.” We need that qualitative understanding to truly keep students on track.

One thing we’ve learned from our analysis of the National Longitudinal Surveys of Youth about these individuals is that it is common for them to report having a bad experience in high school. Important questions for us to consider as we think about how to reach, engage and build an education pathway for these individuals are:

  • “How does their high school experience shape their future interest and expectations for learning?”
  • “What will higher education institutions need to do to counteract these prior experiences so these individuals feel welcome and confident in a path through higher education?”

We will only come to understand this through qualitative inquiry: asking about their experiences, listening to their stories and learning from them.

The value of qualitative data isn’t only critical as more institutions aim to attract, welcome and support students with a greater variety of lived experiences.  A holistic data approach has far-reaching impacts. I’ve seen qualitative data unlock innovative answers in edtech, learning experience design, tuition models, student aid and student connections.

By balancing quantitative and qualitative data, we can advance meaningful solutions that address higher education’s most pressing problems and create a more valuable educational experience for all students.


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