Chris Piech
personal page

Research interests

I 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   
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.   
C. Piech, M. Sahami, J. Huang, and L. Guibas, Autonomously Generating Hints by Inferring Problem Solving Policies, Learning at Scale 2 (2015), 195-204.   
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.   
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.   
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.