IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage

TL;DR

This study compares IP geolocation and mailing addresses in U.S. MOOC registration data, revealing that IP geolocation underestimates economic disparities and biases towards more prosperous areas, affecting research accuracy.

Contribution

It highlights the biases in IP geolocation for economic and geographic analysis of MOOC registrants, emphasizing the need for careful use in research.

Findings

IP geolocation has higher error rates in distressed areas

IP geolocation underestimates regressive registration patterns

Geographic and economic biases affect MOOC registration analysis

Abstract

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 about 76,000 U.S. registrations for about 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.