{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123140"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123140","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Differences in online course usage and IP geolocation bias by local economic profile","abstract":"Although Massive Online Open Courses (MOOCs) have the promise to make rigorous higher education accessible to everyone, prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. In this work, I study geographically granular economic patterns in registration for HarvardX and MITx courses, and in the accuracy of identifying users' locations from their IP addresses. Using ZIP Codes identified by the MaxMind IP geolocation database, I find that per-capita registration rates correlate with economic prosperity and population density. Comparing these ZIP Codes with user-provided mailing addresses, I find evidence of bias in MaxMind geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately geolocates users to prosperous areas; and it underestimates the regressive pattern in MOOC registration. Similar economic biases may affect IP geolocation in other academic, commercial, and legal contexts.","abstract_html":"Although Massive Online Open Courses (MOOCs) have the promise to make rigorous higher education accessible to everyone, prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. In this work, I study geographically granular economic patterns in registration for HarvardX and MITx courses, and in the accuracy of identifying users' locations from their IP addresses. Using ZIP Codes identified by the MaxMind IP geolocation database, I find that per-capita registration rates correlate with economic prosperity and population density. Comparing these ZIP Codes with user-provided mailing addresses, I find evidence of bias in MaxMind geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately geolocates users to prosperous areas; and it underestimates the regressive pattern in MOOC registration. Similar economic biases may affect IP geolocation in other academic, commercial, and legal contexts.","abstract_has_math":false,"creators":["Ganelin, Daniela Ida."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Isaac Chuang."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:11Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/123140"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 82-86)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Although Massive Online Open Courses (MOOCs) have the promise to make rigorous higher education accessible to everyone, prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. In this work, I study geographically granular economic patterns in registration for HarvardX and MITx courses, and in the accuracy of identifying users' locations from their IP addresses. Using ZIP Codes identified by the MaxMind IP geolocation database, I find that per-capita registration rates correlate with economic prosperity and population density. Comparing these ZIP Codes with user-provided mailing addresses, I find evidence of bias in MaxMind geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately geolocates users to prosperous areas; and it underestimates the regressive pattern in MOOC registration. Similar economic biases may affect IP geolocation in other academic, commercial, and legal contexts."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Differences in online course usage and IP geolocation bias by local economic profile"]}]}],"canonical_facts":{"dc:contributor.advisor":["Isaac Chuang."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Ganelin, Daniela Ida."],"dc:date.accessioned":["2019-12-05T18:05:42Z"],"dc:date.available":["2019-12-05T18:05:42Z"],"dc:date.issued":["2019"],"dc:description":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 82-86)."],"dc:description.abstract":["Although Massive Online Open Courses (MOOCs) have the promise to make rigorous higher education accessible to everyone, prior research has shown that registrants tend to come from backgrounds of higher socioeconomic status. In this work, I study geographically granular economic patterns in registration for HarvardX and MITx courses, and in the accuracy of identifying users' locations from their IP addresses. Using ZIP Codes identified by the MaxMind IP geolocation database, I find that per-capita registration rates correlate with economic prosperity and population density. Comparing these ZIP Codes with user-provided mailing addresses, I find evidence of bias in MaxMind geolocation: it makes greater errors, both geographically and economically, for users from more economically distressed areas; it disproportionately geolocates users to prosperous areas; and it underestimates the regressive pattern in MOOC registration. Similar economic biases may affect IP geolocation in other academic, commercial, and legal contexts."],"dc:description.degree":["M. 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