Even before the COVID-19 pandemic dramatically upended higher education, online learning was steadily becoming mainstream. By 2016, more than one-third of students were already taking at least one course online. Online enrollments continued to grow in recent years, even as on-ground enrollment flatlined or decreased. Then the pandemic arrived and entire institutions went fully online virtually overnight.
Behind this period of growth and transformation has been a dramatic increase in the utilization of digital learning technologies, from learning management systems, early alerts, and online proctoring to digital textbooks and other course materials. Lost in the shift to online education has been the power and potential of analytics to improve college access and success.
With a generation of students more technologically-connected than ever in the wake of the pandemic, this new era of digital connectedness surfaces new opportunities—and challenges—as institutions seek to boost student outcomes and engagement. It’s a growing trend we must work to preserve as students return to campus and institutions navigate their new normal.
The use of learning analytics in higher education is not a new thing. It traces its origins back to at least the late 1990’s scholarly research and practice of George Siemens from which many recent applications of learning analytics in higher education are derived. Gradually, institutions acquired the internal capacity—and third-party technologies—to create algorithmic models that became increasingly accurate in forecasting student success and retention in a big data boom in higher education.
By the mid- to late-2010s and with the integration of emerging data science and machine learning techniques, the trend accelerated and growing numbers of colleges and universities were actively leading large-scale—and increasingly complex—institutional analytics efforts.
As it turns out, this work of analyzing the data can also be difficult, time-, people- and resource-intensive. The mass migration to digital classrooms, however, is sparking renewed interest in the potential of learning analytics and predictive modeling for student success.
But this time, given the dramatic increase in the number of learners and faculty using technology regularly for learning, institutional leaders may see that the power of analytics may lie in their simplicity rather than their complexity.
In contrast to the more elaborate analytics systems of the past, institutions are increasingly seeing the value of more accessible—and comparatively simple —data elements. As Karen L. Webber and Henry Y. Zheng wrote in their recent book Big Data On Campuses, many institutions are now data-rich, but information poor.
Collecting and storing large volumes is easier than ever, but knowing what to do with the data, exactly, remains a challenge. The “little data” created through digital courses and other technologies, however, allow institutions to nimbly react in real-time, providing faculty, staff, and students with immediately actionable insights.
As college campuses closed during the pandemic and learning quickly went virtual, the use of online textbooks and other technologies sharply rose. By last fall, spending on digital course materials had increased by 23 percent.
An increasing number of faculty are finding that these digital course materials can be convenient alternatives to traditional print materials. As analog turns to digital, there is a greater capacity to understand how students are engaging with course content and can provide important insights into how their students are learning.
Faculty can use this information to intervene early and ascertain if a student is off track. These are not the kind of aggregate, macro-insights big data provide, but something more personalized, immediate, and easier to parse.
Those insights are not just limited to faculty, however. Advising staff, too, are using analytics to proactively reach out to students and are often even better positioned to see trends and patterns across classes, looking at a student on a more holistic basis.
The Berkeley Online Advising project at the University of California at Berkeley, for example, provides advisors with information that allows for quick intervention when student outcomes are not being met. Tools that give students access to their own learning analytics through easy-to-understand visualizations are also on the rise.
Students at the University of Iowa have access to a student-facing analytics dashboard called Elements of Success, allowing students to better measure and understand their progress, which, in turn, helps motivate them to take action.
As institutions navigate a delicate path back to in-person or hybrid learning, we must not forget the powerful insights made possible by digital course materials and other technologies. We must be cautious about what would be lost by a full return to the status quo. The rise of “little data” may have a dramatic impact on student learning far beyond the pandemic.
Raj Kaji is the CEO of Akademos. Shalini Sealey is the Academic Affairs Coordinator and Institutional Research Analyst at River Parishes Community College.