Research StatementI am interested in using massive amounts of student data to learn how to scale up feedback.
Recent Publications
Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. NIPS 2015: 505-513
@article{piech2015deep,
title={Deep knowledge tracing},
author={Piech, Chris and Bassen, Jonathan and Huang, Jonathan and Ganguli, Surya and Sahami, Mehran and Guibas, Leonidas J and Sohl-Dickstein, Jascha},
journal={Advances in neural information processing systems},
volume={28},
year={2015}
}
C. Piech, J. Huang, A. Nguyen, M. Phulsuksombati, M. Sahami, and L. Guibas, Learning Program Embeddings to Propagate Feedback on Student Code,
International Conference on Machine Learning, 32 (2015), 1093-1102.
@inproceedings{piech2015autonomously,
title={Autonomously Generating Hints by Inferring Problem Solving Policies},
author={Piech, Chris and Sahami, Mehran and Huang, Jonathan and Guibas, Leonidas},
identifier = {phnpsg-lpepf-15}
}
C. Piech, M. Sahami, J. Huang, and L. Guibas, Autonomously Generating Hints by Inferring Problem Solving Policies, Learning at Scale 2 (2015), 195-204.
@inproceedings{piech2015autonomously,
author = {Piech, Chris and Sahami, Mehran and Huang, Jonathan and Guibas, Leonidas},
title = {Autonomously Generating Hints by Inferring Problem Solving Policies},
booktitle = {Proceedings of the Second (2015) ACM Conference on Learning @ Scale},
series = {L@S '15},
year = {2015},
isbn = {978-1-4503-3411-2},
identifier = {pmhg-aghipss-15},
location = {Vancouver, BC, Canada},
pages = {195--204},
numpages = {10},
url = {http://doi.acm.org/10.1145/2724660.2724668},
doi = {10.1145/2724660.2724668},
acmid = {2724668},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {educational datamining., hint generation, problem solving policy},
}
A. Nguyen, C. Piech, J. Huang, and L. Guibas, Codewebs: Scalable Homework Search for Massive Open Online Programming Courses, Proceedings of the 23rd international conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2014.
@inproceedings{nphg-cshsmoopc-14,
author = {Andy Nguyen and Christopher Piech and Jonathan Huang and Leonidas Guibas},
title = {Codewebs: Scalable Homework Search for Massive Open Online Programming Courses},
booktitle = {Proceedings of the 23rd International World Wide Web Conference (WWW 2014)},
year = {2014},
address = {Seoul, Korea}
}
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC,
Jonathan Huang, Chris Piech, Andy Nguyen, Leonidas Guibas.
In the 16th International Conference on Artificial Intelligence in Education (AIED 2013) Workshop on Massive Open Online Courses (MOOCshop)
Memphis, TN, USA, July 2013.
@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}
}
Tuned Models of Peer Assessment in MOOCs,
Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller.
In Proceedings of The 6th International Conference on Educational Data Mining (EDM 2013),
Memphis, TN, USA, July 2013.
@inproceedings{phcdnk-tmpam-13,
author = {Chris Piech and Jonathan Huang and Zhenghao Chen and Chuong Do and Andrew Ng and Daphne Koller},
title = {Tuned Models of Peer Assessment in {MOOC}s},
booktitle = {Proceedings of The 6th International Conference on Educational Data Mining (EDM 2013)},
year = {2013}
}
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