In the first offering of Stanford’s Machine Learning Massive Open-Access Online Course (MOOC) there were over a million programming submissions to 42 assignments — a dense sampling of the range of possible solutions. In this paper we map out the syntax and functional similarity of the submissions in order to explore the variation in solutions. While there was a massive number of submissions, there is a much smaller set of unique approaches. This redundancy in student solutions can be leveraged to “force multiply” teacher feedback.

@inproceedings{hpng-sfvmcsmlm-13, author = {Jonathan Huang and Chris Piech and Andy Nguyen and Leonidas Guibas} title={Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC}, booktitle = {Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013}, year = {2013}, location={Memphis, USA} }