Convolutional Neural Network Applied to Code Assignment Grading

Fábio Rezende Souza, Francisco Zampirolli, Guiou Kobayashi

2019

Abstract

Thousands of students have their assignments evaluated by their teachers every day around the world while developing their studies in any branch of science. A fair evaluation of their schoolwork is a very challenging task. Here we present a method for validating the grades attributed by professors to students programming exercises in an undergraduate introductory course in computer programming. We collected 938 final exam exercises in Java Language developed during this course, evaluated by different professors, and trained a convolutional neural network over those assignments. First, we submit their codes to a cleaning process (by removing comments and anonymizing variables). Next, we generated an embedding representation of each source code produced by students. Finally, this representation is taken as the input of the neural network which classifies each label (corresponding to the possible grades A, B, C, D or F). An independent neural network is trained with source code solutions corresponding to each assignment. We obtained an average accuracy of 74.9% in a 10−fold cross validation for each grade. We believe that this method can be used to validate the grading process made by professors in order to detect errors that might happen during this process.

Paper Citation

in Harvard Style

Rezende Souza F., Zampirolli F. and Kobayashi G. (2019). Convolutional Neural Network Applied to Code Assignment Grading.In Proceedings of the 11th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-367-4, pages 62-69. DOI: 10.5220/0007711000620069

in Bibtex Style

@conference{csedu19,
author={Fábio Rezende Souza and Francisco Zampirolli and Guiou Kobayashi},
title={Convolutional Neural Network Applied to Code Assignment Grading},
booktitle={Proceedings of the 11th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2019},
pages={62-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007711000620069},
isbn={978-989-758-367-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Convolutional Neural Network Applied to Code Assignment Grading
SN - 978-989-758-367-4
AU - Rezende Souza F.
AU - Zampirolli F.
AU - Kobayashi G.
PY - 2019
SP - 62
EP - 69
DO - 10.5220/0007711000620069