| Deconstructing disengagement: analyzing learner subpopulations in massive open online courses RF Kizilcec, C Piech, E Schneider Proceedings of the third international conference on learning analytics and …, 2013 | 1452 | 2013 |
| Deep knowledge tracing C Piech, J Bassen, J Huang, S Ganguli, M Sahami, LJ Guibas, ... Advances in neural information processing systems 28, 2015 | 941 | 2015 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 681 | 2021 |
| Tuned models of peer assessment in MOOCs C Piech, J Huang, Z Chen, C Do, A Ng, D Koller International Conference on Educational Data Mining, 2013 | 521 | 2013 |
| Modeling how students learn to program C Piech, M Sahami, D Koller, S Cooper, P Blikstein Proceedings of the 43rd ACM technical symposium on Computer Science …, 2012 | 242 | 2012 |
| Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming P Blikstein, M Worsley, C Piech, M Sahami, S Cooper, D Koller Journal of the Learning Sciences 23 (4), 561-599, 2014 | 233 | 2014 |
| Learning program embeddings to propagate feedback on student code C Piech, J Huang, A Nguyen, M Phulsuksombati, M Sahami, L Guibas Proceedings of the 32nd International Conference on Machine Learning, Lille …, 2015 | 199 | 2015 |
| Codewebs: scalable homework search for massive open online programming courses A Nguyen, C Piech, J Huang, L Guibas Proceedings of the 23rd international conference on World wide web, 491-502, 2014 | 147 | 2014 |
| Achieving fairness through adversarial learning: an application to recidivism prediction C Wadsworth, F Vera, C Piech arXiv preprint arXiv:1807.00199, 2018 | 136 | 2018 |
| Autonomously generating hints by inferring problem solving policies C Piech, M Sahami, J Huang, L Guibas Proceedings of the second (2015) acm conference on learning@ scale, 195-204, 2015 | 116 | 2015 |
| Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning L Wang, A Sy, L Liu, C Piech Proceedings of the 10th International Conference on Educational Data Mining;, 2017 | 94 | 2017 |
| Syntactic and functional variability of a million code submissions in a machine learning mooc J Huang, C Piech, A Nguyen, L Guibas AIED 2013 Workshops Proceedings Volume 25, 2013 | 93 | 2013 |
| Deep knowledge tracing on programming exercises L Wang, A Sy, L Liu, C Piech Proceedings of the fourth (2017) ACM conference on learning@ scale, 201-204, 2017 | 85 | 2017 |
| The future of data-enriched assessment. C Thille, E Schneider, RF Kizilcec, C Piech, SA Halawa, DK Greene Research & Practice in Assessment 9, 5-16, 2014 | 67 | 2014 |
| K means C Piech Internet: http://stanford. edu/~ cpiech/cs221/handouts/kmeans. html,[Mar 05 …, 2013 | 45 | 2013 |
| Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference M Wu, M Mosse, N Goodman, C Piech AAAI Conference on Artificial Intelligence, 2019 | 44 | 2019 |
| Variational item response theory: Fast, accurate, and expressive M Wu, RL Davis, BW Domingue, C Piech, N Goodman arXiv preprint arXiv:2002.00276, 2020 | 38 | 2020 |
| TMOSS: Using intermediate assignment work to understand excessive collaboration in large classes L Yan, N McKeown, M Sahami, C Piech Proceedings of the 49th ACM technical symposium on computer science …, 2018 | 28 | 2018 |
| Pensieve: Feedback on coding process for novices L Yan, A Hu, C Piech Proceedings of the 50th acm technical symposium on computer science …, 2019 | 25 | 2019 |
| Grades are not Normal: Improving Exam Score Models Using the Logit-Normal Distribution N Arthurs, B Stenhaug, S Karayev, C Piech Educational Data Mining, 2019 | 22 | 2019 |
Mehran SahamiProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
