I’ve been an avid NFL fan since the Dolphins went undefeated in 1972. For 30 of those years, I served public and higher education, retiring recently as a campus president. Whether you are a fan or not, we can learn a lot from this league’s recent commitment to deep analytics. As education leaders and practitioners, most of us are well aware of the importance of statistics, as we continue to improve student success and address equity and inclusion. We’ve been looking to our metrics throughout the decades. So, what’s so special about the NFL and their recent evolution to what they call “Next Gen Stats?” With an estimated fan base of 200 million worldwide, one could argue they are ten times the size of all higher education in the U.S. alone. When they partnered with Amazon Web Services, they set the goal of engaging every fan in every game on every play!
Let me say that again: The NFL has set the lofty goal to leverage deep analytics to engage every fan in every game on every play. Clearly, they don’t think they can immerse their millions of fans that deeply. But make no mistake—they understand and value engagement. You see, it’s a question of probability. They have embraced the fact that deep analytics powered by machine learningoffers a higher probability of winning the game—and engaging fans! The NFL has lately taken to calling this “Stat That.” In our world of higher education, we derive, maintain and compile our own deep analytics with the similar goal of helping every student succeed. But what would happen in a new higher ed landscape, where every student is tracked across every learning object, every day?
It’s not enough to generate grade warnings and place students on probation when they drop below a GPA threshold. It’s not enough to encourage our faculty to design courses with formative and summative assessments that function similarly, automating alerts when students miss key assignments or perform poorly on a major exam. These alerts and warnings are so often sent out in the wrong format (emails) to an audience engaged on far different platforms. Until we set the goal of engaging every student across every learning object and generate and share those predictive analytics with students in the form of “nudges,” we are missing a major opportunity to engage that base.
Such a shift across all courses for all students would take years! Happily, modern Learning Management Systems (LMS) are designed to begin this journey, and the recent mass migration to remote learning brought on by the global pandemic has changed core assumptions. As the consumer and media markets further deploy these analytics and take personalization to unprecedented levels, so too must higher education. In the post-pandemic world student assumptions have changed as well. Personally, I don’t see students as customers or fans. I am sold on the engagement aspect. We would do well to optimize every learning opportunity for each one as we continue to press for equity and inclusion.
We need more and more data across our increasingly diverse base of stakeholders. Imagine a world in which every student can achieve each learning outcome in no less than eight different ways. Further, envision how each student’s success across different learning objectives is tracked for patterns of preferences. Your institution’s LMS works with the Learning Object Repository (LOR) and the layer of analytics (ideally designed by your faculty) to serve target objects to target students each day. Students receive daily nudges across the communication platforms they use. Faculty benefit from more time to design engaging learning experiences as the LMS/LOR personalize course experiences (again, across a taxonomy owned by faculty). Administrators celebrate increases in retention and attainment (and if your unions or senate is on the ball, an appreciable percentage of these gains are reflected in instructional and other pay scales). If this sounds like utopia, read on.
When I left my role as campus president in South Florida, we were investing in the redesign of over 300 courses for one objective: deeper levels of student engagement by design. Faculty were compensated for this redesign to ensure ownership. Each learning object was curated for engagement. At another college I loaded my faculty based on class size, with automated course elements (again, designed or selected by faculty) yielding class sizes determined by faculty according to their chosen level of engagement (aligned with union requirements). At yet another college, our faculty senate approved a rubric for all online courses setting class sizes across five Levels of Engagement. My point here is that this journey is not only already underway, it is leading us to the inevitable goal of ultra-personalization (every student, every object, every day).
As you work with your colleagues, unions, leadership and even legislators, you will invariably become engaged in this work toward ultra-personalization across higher education. In our post pandemic reality amidst a long-overdue focus on equity and inclusion, we need such systemic solutions more than ever. Deep analytics can not only tell the NFL the completion probability of a given receiver on any down and distance; deep analytics can tell us the attainment probability of any student on any given day. We can then derive intervention taxonomies based on predictive analytics and engage those students before they reach a turning point. Sometimes you need academic ability or another opportunity to achieve a learning outcome a bit differently. It could be that the course was being presented in a manner that didn’t make sense to them given their cultural background. Perhaps they need a micro-loan or gas card. Maybe a bit of counselling at just the right moment or help with childcare. Or, they just might benefit from consistent nudges gradually pushing them to higher levels of maturity and responsibility. It all begins with increased engagement.
Whatever the barrier to their success, believe me, you can Stat That.
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