MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Analyzing student learning trajectories in an introductory programming MOOC

Author(s)
Bajwa, Ayesha R.(Ayesha Raji)
Thumbnail
Download1127389824-MIT.pdf (2.867Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Una-May O'Reilly and Erik Hemberg.
Terms of use
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
Metadata
Show full item record
Abstract
Understanding student learning and behavior in Massive Open Online Courses (MOOCs) can help us make online learning more beneficial for students. We investigate student learning trajectories on the individual problem level in an MITx MOOC teaching introductory programming in Python, considering simple features of the student and problem as well as more complex keyword occurrence trajectory features associated with student code submissions. Since code is so problem-specific, we develop gold standard solutions for comparison. Anecdotal observations on individual student trajectories reveal distinct behaviors which may correlate with prior experience level. We build models to correlate these trajectories with student characteristics and behaviors of interest, specifically prior experience level and video engagement. Generative modeling allows us to probe the space of submitted solutions and trajectories and explore these correlations.
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 73-75).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/123002
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.