Understanding learner engagement and the effect of course structure in massive open online courses

In this thesis, we analyze learner performance in two edX programming courses. We look at many dierent types of learners, such as learners who have taken both intro and advanced courses, learners who opt to pay for certification, learners who take the experimental self-paced course, learners who eventually become community teaching assistants, and learners who take the course after the implementation of gating. In particular, we focus on repeat learners, or learners who have taken the course multiple times. When courses undergo a change from semester to semester, for example changing the pacing of the course or making certain content only available to paid users, it can be very useful to look at learners who were in the course before and after this change. Our goal is to gain a baseline understanding of how dierent factors affect learner behavior and how a few changes that edX has made to courses affect learner performance. With the best understanding of how learners interact with and complete courses, edX instructors will be able to provide the best possible online education experience for their learners.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Vonder Haar, Christine M.

Advisor dc:contributor.advisor

• Ana Bell.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129929

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129929