IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage
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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery (ACM)
2021
|
| Online Access: | https://hdl.handle.net/1721.1/129754 |
| _version_ | 1826207767883415552 |
|---|---|
| author | Ganelin, Daniela Chuang, Isaac L. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ganelin, Daniela Chuang, Isaac L. |
| author_sort | Ganelin, Daniela |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:54:38Z |
| format | Article |
| id | mit-1721.1/129754 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:54:38Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1297542022-09-28T17:00:19Z IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage Ganelin, Daniela Chuang, Isaac L. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. 2021-02-12T16:08:53Z 2021-02-12T16:08:53Z 2019-10 2020-12-04T19:43:01Z Article http://purl.org/eprint/type/ConferencePaper 9781450372541 https://hdl.handle.net/1721.1/129754 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. en http://dx.doi.org/10.1145/3369255.3369301 ICETC 2019: Proceedings of the 2019 11th International Conference on Education Technology and Computers Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) arXiv |
| spellingShingle | Ganelin, Daniela Chuang, Isaac L. IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title | IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title_full | IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title_fullStr | IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title_full_unstemmed | IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title_short | IP Geolocation Underestimates Regressive Economic Patterns in MOOC Usage |
| title_sort | ip geolocation underestimates regressive economic patterns in mooc usage |
| url | https://hdl.handle.net/1721.1/129754 |
| work_keys_str_mv | AT ganelindaniela ipgeolocationunderestimatesregressiveeconomicpatternsinmoocusage AT chuangisaacl ipgeolocationunderestimatesregressiveeconomicpatternsinmoocusage |