Who does what in a massive open online course?
Massive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants behaved in 6.002x...
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| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110801 https://orcid.org/0000-0001-7296-523X https://orcid.org/0000-0001-5697-1496 |
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| author | Seaton, Daniel Bergner, Yoav Chuang, Isaac Mitros, Piotr Pritchard, David E |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Seaton, Daniel Bergner, Yoav Chuang, Isaac Mitros, Piotr Pritchard, David E |
| author_sort | Seaton, Daniel |
| collection | MIT |
| description | Massive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants
behaved in 6.002x - Circuits and Electronics, the first course in MITx (now edX). We divide participants into tranches based on the extent of their assessment activities, ranging from browsers (who constituted ~
76% of the participants but accounted for only 8% of the total time
spent in the course) to certificate-earners (7% of theparticipants
who accounted for 60% of the total time). We examine how the certificate
earners allocated their time amongst the various course components and study what fraction of each they accessed. We analyze transitions
between course components, showing, how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how use of various course components, and transitions
among them, influence learning in MOOCs. |
| first_indexed | 2024-09-23T16:19:19Z |
| format | Article |
| id | mit-1721.1/110801 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:19:19Z |
| publishDate | 2017 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1108012022-10-02T07:43:34Z Who does what in a massive open online course? Seaton, Daniel Bergner, Yoav Chuang, Isaac Mitros, Piotr Pritchard, David E Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Physics Massachusetts Institute of Technology. Office of Digital Learning Massachusetts Institute of Technology. Research Laboratory of Electronics Seaton, Daniel Bergner, Yoav Chuang, Isaac Mitros, Piotr Pritchard, David E Massive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants behaved in 6.002x - Circuits and Electronics, the first course in MITx (now edX). We divide participants into tranches based on the extent of their assessment activities, ranging from browsers (who constituted ~ 76% of the participants but accounted for only 8% of the total time spent in the course) to certificate-earners (7% of theparticipants who accounted for 60% of the total time). We examine how the certificate earners allocated their time amongst the various course components and study what fraction of each they accessed. We analyze transitions between course components, showing, how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how use of various course components, and transitions among them, influence learning in MOOCs. National Science Foundation (U.S.) (DUE-1044294) 2017-07-21T15:37:51Z 2017-07-21T15:37:51Z 2014-04 Article http://purl.org/eprint/type/JournalArticle 0001-0782 http://hdl.handle.net/1721.1/110801 Seaton, Daniel T.; Bergner, Yoav; Chuang, Isaac et al. “Who Does What in a Massive Open Online Course?” Communications of the ACM 57, 4 (April 2014): 58–65 https://orcid.org/0000-0001-7296-523X https://orcid.org/0000-0001-5697-1496 en_US http://dx.doi.org/10.1145/2500876 Communications of the ACM Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) Other repository |
| spellingShingle | Seaton, Daniel Bergner, Yoav Chuang, Isaac Mitros, Piotr Pritchard, David E Who does what in a massive open online course? |
| title | Who does what in a massive open online course? |
| title_full | Who does what in a massive open online course? |
| title_fullStr | Who does what in a massive open online course? |
| title_full_unstemmed | Who does what in a massive open online course? |
| title_short | Who does what in a massive open online course? |
| title_sort | who does what in a massive open online course |
| url | http://hdl.handle.net/1721.1/110801 https://orcid.org/0000-0001-7296-523X https://orcid.org/0000-0001-5697-1496 |
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