IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage
Author(s)Ganelin, Daniela; Chuang, Isaac L.
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Massive open online courses (MOOCs) promise to make rigorous higher education accessible to everyone, but prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. We study geographically granular economic patterns in ~76,000 U.S. registrations for ~600 HarvardX and MITx courses between 2012 and 2018, identifying registrants' locations using both IP geolocation and user-reported mailing addresses. By either metric, we find higher registration rates among postal codes with greater prosperity or population density. However, we also find evidence of bias in IP geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately places users in prosperous areas; and it underestimates the regressive pattern in MOOC registration. Researchers should use IP geolocation in MOOC studies with care, and consider the possibility of similar economic biases affecting its other academic, commercial, and legal uses.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
ICETC 2019: Proceedings of the 2019 11th International Conference on Education Technology and Computers
Association for Computing Machinery (ACM)
Ganelin, Daniela and Isaac Chuang. "IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage." ICETC 2019: Proceedings of the 2019 11th International Conference on Education Technology and Computers, October 2019, Amsterdam, Netherlands, Association for Computing Machinery, October 2019.
Author's final manuscript