Name
Abstract | ||
|---|---|---|
Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University's CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions. |
Year | Venue | Field |
|---|---|---|
2015 | International Conference on Machine Learning | ENCODE,Computer science,Precondition,Theoretical computer science,Artificial intelligence,Linear map,Artificial neural network,Postcondition,Machine learning |
DocType | Volume | Citations |
Journal | abs/1505.05969 | 30 |
PageRank | References | Authors |
1.22 | 19 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Piech | 1 | 616 | 54.21 |
| Jonathan Huang | 2 | 777 | 47.66 |
| Andy Nguyen | 3 | 154 | 6.01 |
| Mike Phulsuksombati | 4 | 30 | 1.22 |
| Mehran Sahami | 5 | 4138 | 556.74 |
| Leonidas J. Guibas | 6 | 13084 | 1262.73 |