Analyzing student learning trajectories in an introductory programming MOOC
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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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/123002 |
| _version_ | 1826200624814882816 |
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| author | Bajwa, Ayesha R.(Ayesha Raji) |
| author2 | Una-May O'Reilly and Erik Hemberg. |
| author_facet | Una-May O'Reilly and Erik Hemberg. Bajwa, Ayesha R.(Ayesha Raji) |
| author_sort | Bajwa, Ayesha R.(Ayesha Raji) |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T11:39:20Z |
| format | Thesis |
| id | mit-1721.1/123002 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:39:20Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1230022019-11-22T03:23:03Z Analyzing student learning trajectories in an introductory programming MOOC Bajwa, Ayesha R.(Ayesha Raji) Una-May O'Reilly and Erik Hemberg. 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 73-75). 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. by Ayesha R. Bajwa. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-22T00:01:10Z 2019-11-22T00:01:10Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123002 1127389824 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 75 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Bajwa, Ayesha R.(Ayesha Raji) Analyzing student learning trajectories in an introductory programming MOOC |
| title | Analyzing student learning trajectories in an introductory programming MOOC |
| title_full | Analyzing student learning trajectories in an introductory programming MOOC |
| title_fullStr | Analyzing student learning trajectories in an introductory programming MOOC |
| title_full_unstemmed | Analyzing student learning trajectories in an introductory programming MOOC |
| title_short | Analyzing student learning trajectories in an introductory programming MOOC |
| title_sort | analyzing student learning trajectories in an introductory programming mooc |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/123002 |
| work_keys_str_mv | AT bajwaayesharayesharaji analyzingstudentlearningtrajectoriesinanintroductoryprogrammingmooc |