Abstract
MOOCs allow thousands of students to be taught how to
program at scale and, using automatic error correction, personalized
feedback can also be provided at scale. Current NLP techniques have
been used to correct errors in programming assignments, however, these
mainly focus on syntactic errors and do not take advantage of NLU
techniques to understand the desired semantics. By understanding se-
mantics, there is a potential that the accuracy of error corrections can
be vastly improved as focus shifts to correcting errors specific to the de-
sired problem, rather than errors common across all programs. Each step
of the process also gives an opportunity to do surrounding work on NLU
techniques applied to source code.
program at scale and, using automatic error correction, personalized
feedback can also be provided at scale. Current NLP techniques have
been used to correct errors in programming assignments, however, these
mainly focus on syntactic errors and do not take advantage of NLU
techniques to understand the desired semantics. By understanding se-
mantics, there is a potential that the accuracy of error corrections can
be vastly improved as focus shifts to correcting errors specific to the de-
sired problem, rather than errors common across all programs. Each step
of the process also gives an opportunity to do surrounding work on NLU
techniques applied to source code.
Original language | English |
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Number of pages | 4 |
Publication status | Published - 18 Sept 2017 |
Event | 10th European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases 2017 - Skopje, Macedonia, The Former Yugoslav Republic of Duration: 18 Sept 2017 → 22 Sept 2017 http://ecmlpkdd2017.ijs.si/ |
Conference
Conference | 10th European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases 2017 |
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Abbreviated title | ECML PKDD 2017 |
Country/Territory | Macedonia, The Former Yugoslav Republic of |
City | Skopje |
Period | 18/09/17 → 22/09/17 |
Internet address |
Keywords
- automatic error correction
- natural language processing