Differences in online course usage and IP geolocation bias by local economic profile

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.

Bibliographic Details
Main Author: Ganelin, Daniela Ida.
Other Authors: Isaac Chuang.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:https://hdl.handle.net/1721.1/123140
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author Ganelin, Daniela Ida.
author2 Isaac Chuang.
author_facet Isaac Chuang.
Ganelin, Daniela Ida.
author_sort Ganelin, Daniela Ida.
collection MIT
description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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spelling mit-1721.1/1231402019-12-06T03:24:30Z Differences in online course usage and IP geolocation bias by local economic profile Ganelin, Daniela Ida. Isaac Chuang. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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). 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. by Daniela Ida Ganelin. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-12-05T18:05:42Z 2019-12-05T18:05:42Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123140 1128823097 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 95 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Ganelin, Daniela Ida.
Differences in online course usage and IP geolocation bias by local economic profile
title Differences in online course usage and IP geolocation bias by local economic profile
title_full Differences in online course usage and IP geolocation bias by local economic profile
title_fullStr Differences in online course usage and IP geolocation bias by local economic profile
title_full_unstemmed Differences in online course usage and IP geolocation bias by local economic profile
title_short Differences in online course usage and IP geolocation bias by local economic profile
title_sort differences in online course usage and ip geolocation bias by local economic profile
topic Electrical Engineering and Computer Science.
url https://hdl.handle.net/1721.1/123140
work_keys_str_mv AT ganelindanielaida differencesinonlinecourseusageandipgeolocationbiasbylocaleconomicprofile