There was a lot of hype around big data in higher education in 2015. Colleges and universities, inundated with data from legacy systems and incentivized by renewed accountability pressures, have begun to link disparate information from across the campus. Historically limited to transactional data from registrars and student information systems, the application of data-driven decision making has begun to permeate all aspects of campus life and operations—as enterprising leaders harness predictive analytics to tackle bottleneck courses, power advising initiatives and share best practices with their peers.
Despite the hype, the field remains nascent, the implications uncertain. Big questions remain: How will a more sophisticated understanding of outcomes inform accountability models that incentivize universities to serve a more diverse student population? Will existing privacy and security policies adapt to provide students with the protections they need amid higher ed’s big data explosion? What winners and losers will emerge among today’s “hot,” venture backed edtech startups? When we look back on 2016, we may very well see it as the jumping-off point for policies and practices that define higher education in the digital era. Here are four predictions to watch for in the coming year:
What winners and losers will emerge from big data's arrival in higher ed?
1. Big data becomes useful. 2016 will be a defining year for big data in higher education as colleges and universities follow the lead of early pioneers, and begin to link risk indicators to interventions that work at scale. Big data is already beginning to impact operations and staffing, with the inclusion of words like “predictive analytics” in job titles, following the recent onslaught of “innovation” focused job postings.
This new cadre of higher ed technology leaders transcend the IT department to focus on issues like retention, affordability, and the delivery of content and courses through new modalities. Some institutions already have a head start. Over the last decade, Georgia State coupled data analytics with college advising to eliminate the gap in graduation rates between low-income and minority students and the rest of its student body, while also raising their overall graduation rate by 22 points.
2. Policymakers will take notice. At both the federal and state levels, big data will provide policymakers with unprecedented transparency to inform longstanding debates about the value, equity, and affordability of higher education. For years, Congress has asked for Pell completion, rather than just participation rates. But compiling that data required a complex, manual process by the U.S. Department of Education. No more: integration of legacy enrollment management and student information systems means that we are, for the first time, beginning to understand completion rates for low income students – which should have big implications for policymaking.
Over the next year, we will also see early returns from the Obama Administration’s First in the World competition – as some of our nation’s largest universities test the impact of data-informed advising on student outcomes. At the state level, look out for “performance funding 2.0” during 2016 legislative sessions as institutional leaders use big data to make the case for public investment – and more granular outcome data enables legislators to reward universities that demonstrate success with an increasingly diverse student population. States like Florida and Tennessee are already leading the way with new performance-based funding policies.
3. Data privacy and security concerns spike. Higher ed’s big data investment and the consumerization of edtech will converge to make unprecedented data available to students and families. More and more student data will move to the cloud. Students, empowered to make better decisions among and within institutions, will take ownership for their success—and hopefully begin to bend the curve on outcomes. But they’ll increasingly voice concerns about data privacy and security, as more of their information makes its way to the cloud.
State policymakers, university leaders, and entrepreneurs will have to set new standards and expectations for data in this new era. State regulators and auditors will begin to clarify some basic compliance standards for data collection, management, security, interoperability, privacy, and more. These new rules are a welcome sign that education technology and data is truly coming of age.
Collaboration is king. As institutions begin to talk about results, scale will be the primary focus for 2016. To impact outcomes at scale, universities will need to set aside competition and embrace collaboration. Technology firms will also grapple with collaboration, as they partner with institutional leaders to define the rules of the road for higher education in the digital era. College and university leaders will vocalize concerns about “data jail” and interoperability.
In response, the business models for tech firms will evolve. Sharing best practices, and even de-identified data, will allow institutions to tap into new insights about how to help struggling students. Collaboratives focused on student success are already starting to foster this kind of sharing of best practices.
As executive director of the University Innovation Alliance, Bridget Burns leads a national consortium of large public research universities collaborating to improve outcomes for students across the socioeconomic spectrum through innovation, scale, and diffusion of best practices. For the past decade, she has advised university presidents, chancellors, and policymakers on higher education policy, strategy and innovation. Follow @BBurnsEDU
