Data Analytics in Higher Education: Key Concerns and Open Questions

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APA Citation

Rubel, A., & Jones, K. M. L. (2017). Data analytics in higher education: Key concerns and open questions. University of St. Thomas Journal of Law and Public Policy, 11(1), 2544.


“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas.

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Scholarly Citations

Jones, K. M. L. (2018). Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 1–22. doi: 10.1007/s10639-018-9781-8

Vaidya, A., Shiwaikar, S., Munde, V., & Bafana, P. (2017). CMM-based quality management model for teaching and learning process. International Journal of Software Engineering and Its Application, 11(11), 17–30. doi: 10.14257/ijseia.2017.11.11.02

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Digital Commons Network. (2017, May). Higher education: “Data analytics in higher education: Key concerns and open questions (Rubel & Jones). Higher Education Commons, 65. Retrieved from

Digital Commons Network. (2017). Privacy law: “Data analytics in higher education: Key concerns and open questions (Rubel & Jones). Privacy Law Commons. Retrieved from