Feature engineering for clustering student solutions
Name
Miller_Feature engineering.pdf
Size
87.67 KB
Format
Adobe PDF
Checksum (MD5)
8d6badc29102f366091ce99a58d633ae
Author(s)
Glassman, Elena L.
Singh, Rishabh
Miller, Robert C.
Date Issued
March 2014
Journal
Proceedings of the first ACM conference on Learning @ scale conference (L@S '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Elena L. Glassman, Rishabh Singh, and Robert C. Miller. 2014. Feature engineering for clustering student solutions. In Proceedings of the first ACM conference on Learning @ scale conference (L@S '14). ACM, New York, NY, USA, 171-172.
Version
Author's final manuscript
Abstract
Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchical clustering of student solutions to first partition them based on the choice of algorithm and then partition solutions implementing the same algorithm based on low-level implementation details. Our initial investigations in domains of introductory programming and computer architecture demonstrate that we need two different classes of features to perform effective clustering at the two levels, namely abstract features and concrete features.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2556325.2567865
