Dancing with Data in Higher Education

Matt Orourke Blog Catalytic Conversations

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Most leaders in higher education came of age in a day when dancing with data had different steps. Their dance with data was important, make no mistake. Getting solid data for boards, state regulators, federal agencies, and accreditors was at best required and at worst existentially threatening. Institutional Research (IR) departments took their work seriously; so much so, they were typically overwhelmed with requirements and particularly protective of their products and processes. Progressive data-centric colleges collected Key Performance Indicators (KPIs) and followed emerging Continuous Quality Improvement (CQI) traditions. For example, the Continuous Quality Improvement Network (CQIN), an early and effective data-dancing organization, had colleges that won the Baldridge Award (e.g., Richland College), were featured in key publications (e.g., Community College of Denver in ‘Embracing the Tiger’), and who were shining examples of the courage to learn and guide organizational change with data. This data dance is no less important today, Indeed, reporting requirements are not only still there, they are expanding. However, as leaders across higher education are seeing, the embrace of digital tools for student information systems (SIS), learning management systems (LMS), customer relationship management systems (CRM, recruitment and advising tools), digital curricula, and student apps brings a flood of new and deeper data that holds far more potential than producing accurate and useful reports for planning and monitoring. Bringing together modern data science—e.g., predictive modeling, machine learning, and sentiment analysis—and strategic design thinking means that we can bring data to life not only to tell us stories about students today and past, but about the likely trajectories our students are heading toward on their learning and completion paths and how we might improve those trajectories. Let’s think of this new dance as a Data Waltz. As you might know, a traditional waltz is a dance in triple meter that consists of three key steps that cycle into an array of different forms. Dance innovators have taken the basic waltz and created untold innovations, including the Viennese Waltz, Slow Waltz, Scandinavian Waltz, and the Contemporary Western Waltz. To keep with the metaphor, in any good Data Waltz there three basic steps that allow leaders bring new energy and insight to their work and afford the ability to create their own innovations on the base. Here we go: Step One: move away from a primary focus on reporting. While your traditional IR work is vital, it should not be the sole data driver. Indeed, you need to take a bold step out and away from a primary focus on reporting and into the world of real-time and predictive data, which involves broader teams. This means building out your infrastructure to turn your own data lights on (not rely on best practice data from other places) and starting to share the data with larger groups who are more focused on guiding operations rather than precise reporting. This is bold move that includes honoring your past as you move to your future. Moreover, it’s a courageous step, because sometimes sharing the data broadly, especially data that aren’t flattering, means working hard to keep a culture of wonder at the core, as opposed to an easy and often damaging culture of blame—e.g., “who’s fault is that!” Step Two: put these new data tools on purpose, and quickly. Sometimes the bold first step stalls the dance as teams look in wonder at their new data and get trapped in analysis paralysis. You must push to thoughtfully, but assuredly, make the second step. Second-step work means using your real-time and predictive data about your students to reach out, make contact, and act—typically focused on improving persistence and completion outcomes. However, future innovations in this dance will hopefully include more inclusive, interesting, and expansive targets like optimizing the student learning experience, linking to career outcomes, and long-term personal welfare/agency. This second step can include more complicated movements, like adopting an app for faculty, advisors, or students, or redesigning pathways; or simple steps like assembling care teams to guide triage and outreach or launching nudge campaigns to reach out to students at the right time with the right message to keep them on the right track. Regardless of the complexity, this second step has to be taken with care as the misuse or unethical use of these data can be problematic (see New America’s excellent report on these issues). Step Three: bring it together with learning. While steps one and two are the boldest and most obvious, it’s step three that is the most neglected. And as any good waltz pro will tell you, the third step sets you up for keeping the cycle going and really innovating. Indeed, taking the time to test what is working in your student-success data dance using solid analysis matters. Using impact studies on outcomes—particularly unpacking equity gaps in student outcomes—is a must. So too is challenging yourself to hold this learning to the highest standard. Good leaders never neglect the third step in this dance. Indeed, it’s their way of charting the course for the continuing flow of their work. Seasoned dance teachers will tell you, dancing takes courage and practice. We’ll need them both as we continue to adopt this new data dance in higher education. Moreover, we’ll need leaders ready, willing, and able to cue the music and get on the floor! An earlier version of this post first appeared as an article in Ferris State University’s “Perspectives” publication. It’s a sister piece to the lead article on the student-success transformation by Dr. Marcia Ballinger of Lorain County Community College. The full publication with both articles is available here. 

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