Analyzing student learning trajectories in an introductory programming MOOC

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.

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

2019

Author dc:creator

• Bajwa, Ayesha R.(Ayesha Raji)

Advisor dc:contributor.advisor

• Una-May O'Reilly and Erik Hemberg.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

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/1721.1/123002

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123002