Big Data

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Big Data is at the heart of modern science and business.[1]

~ Francis X. Diebold, University of Pennsylvania

            MOOCs and other online education spaces generate tremendous amounts of data about student behavior, learning styles, and interactions with course material, teachers, and other students.  MOOCs also provide data about time spent on particular assignments and engagement in general with the MOOC environment.  We can know when students are online and offline, and for how long. Ostensibly, analysis of this data can teach us a great deal about learning. This possibility has people trumpeting Big Data as the next big thing.

Big data services are already all around us.  Google and Amazon collect and analyze tremendous amounts of data on their users and customers. Government agencies use big data and analytics to identify patterns of behavior. In general, government and business expect Big Data to help drive decision-making with data and analysis rather than intuition and experience.

With the advent of online learning, higher education is now following the lead of government agencies and for-profit corporations by dipping its toe in the big-data waters. In 2011, Ganesan (Ravi) Ravishanker, Chief Information Officer at Wellesley College, published “Doing Academic Analytics Right: Intelligent Answers to Simple Questions,” in the ECAR Research Bulletin, published by Educause. Ravishanker argues that “data-driven decision-making is ever more essential.” He goes on to say that institutions will do well to encourage systemic interaction with data reports as part of the process of ensuring a return on their investment, and that applying data analytics to institutional learning environments is an opportunity missed on most campuses. With respect to learning management systems and the volumes of interpretable data they represent, Ravishanker encourages campus leaders to question “student and faculty access patterns, how many artifacts are associated with a course, how are students and faculty using the system.”[2]

Similarly, in 2012, IBM and Campus Technology published “Building a Smarter Campus: How Analytics is Changing the Academic Landscape,” reporting that higher ed institutions are increasingly recording the events, activities and assignments of their students.  Implementing tools to analyze that data will give decision-makers the ability to predict learning outcomes and better attend to individual needs: “As the amount of data in higher education is increasing exponentially, data analytics is fast becoming the process-of-choice for colleges and universities that want to improve student learning and campus operations. By turning masses of data into useful and actionable intelligence, higher education institutions are creating smarter campuses—for now and for the future.”[3]

Educause, in its 2012 “Study of Analytics in Higher Education,” defines analytics as the “use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues.”[4] Leaders in higher education are increasingly aware of analytical tools at their disposal. “Predictive tools” help analyze what has happened in a given scenario in order to understand what is likely to happen in the next one; “prescriptive tools” then provide recommendations on how best to respond. Such analytics display patterns in student-generated data and project potential outcomes, allowing for informed decisions based on solid projections rather than on intuition.

MOOCs—and even “traditional” local classroom learning systems—produce massive amounts of data that we may well want to analyze with these tools, in the hopes of improving learning outcomes. Online learning systems, aggregations of data, and the availability of analytics could be converging to rewire the teaching-and-learning circuitry. That potential is driving new vendors to target higher education. A brief review of some emerging players and their products highlights the scope of this emerging academic support industry.


[1] Francis X. Diebold, “A Personal Perspective on the Origin(s) and Development of ‘Big Data’: The Phenomenon, the Term, and the Discipline” (University of Pennsylvania, November 26, 2012).

[2] Ganesan (Ravi) Ravishanker, “Doing Academic Analytics Right: Intelligent Answers to Simple Questions,” ECAR Research Bulletin 2 (2011).

[3] IBM, “Building a Smarter Campus- How Analytics Is Changing the Academic Landscape” (1105 Media. Education Group, January 23, 2012), http://public.dhe.ibm.com/common/ssi/ecm/en/ytl03072usen/YTL03072USEN.PDF.

[4] Jacqueline Bichsel, “Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations” (EDUCAUSE CENTER FOR APPLIED RESEARCH, 2012).

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