Analyzing Student Code Trajectories in an Introductory Programming MOOC

Understanding student behavior in Massive Open Online Courses (MOOCs) can help us make online learning more beneficial for students. We investigate student code trajectories on the individual problem level in an MITx MOOC teaching introductory programming in Python, using keyword occurrence features...

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Bibliographic Details
Published in2019 IEEE Learning With MOOCS (LWMOOCS) pp. 53 - 58
Main Authors Bajwa, Ayesha, Bell, Ana, Hemberg, Erik, O'Reilly, Una-May
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Computer aided instruction
Correlation
Electronic learning
Gold
learning trajectories
MOOCs
program features
Python
Trajectory
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Summary:Understanding student behavior in Massive Open Online Courses (MOOCs) can help us make online learning more beneficial for students. We investigate student code trajectories on the individual problem level in an MITx MOOC teaching introductory programming in Python, using keyword occurrence features associated with code submissions to represent these trajectories. 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.
DOI:10.1109/LWMOOCS47620.2019.8939666