Student Code Trajectories in an Introductory Programming MOOC
Ayesha Bajwa, Erik Hemberg, Ana Bell, Una-May O’Reilly · 2019
In classrooms, instructors teaching students how to code have the ability to monitor progress and provide feedback through regular interaction. There is generally no analogous tracing of learning progression in programming MOOCs, hindering the ability of MOOC platforms to provide automated feedback at scale. We explore features for every certified student's history of code submissions to specific problems in a programming MOOC and measure similarity to sample solutions. We seek to understand whether students who succeed in the course reach solutions similar to these instructor-intended sample solutions, in terms of the concepts and mechanisms they contain. Furthermore, do students learn to conform to instructor expectations as the course progresses, and does prior experience have correlations with student behavior? We also explore what feature representations are sufficient for code submission history, since they are directly applicable to the development of automated tutors for progress tracking.