Modeling programming knowledge for mentoring at scale
Author(s)Pai, Anvisha H.; Guo, Philip J.; Miller, Robert C.
MetadataShow full item record
In large programming classes, MOOCs or online communities, it is challenging to find peers and mentors to help with learning specific programming concepts. In this paper we present first steps towards an automated, scalable system for matching learners with Python programmers who have expertise in different areas. The learner matching system builds a knowledge model for each programmer by analyzing their authored code and extracting features that capture domain knowledge and style. We demonstrate the feasibility of a simple model that counts the references to modules from the standard library and Python Package Index in a programmers' code. We also show that programmers exhibit self-selection using which we can extract the modules a programmer is best at, even though we may not have all of their code. In our future work we aim to extend the model to encapsulate more features, and apply it for skill matching in a programming class as well as personalizing answers on StackOverflow.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the first ACM conference on Learning @ scale conference (L@S '14)
Association for Computing Machinery (ACM)
Anvisha H. Pai, Philip J. Guo, and Robert C. Miller. 2014. Modeling programming knowledge for mentoring at scale. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 181-182.
Author's final manuscript