loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Fábio De Rezende Souza ; Francisco de Assis Zampirolli and Guiou Kobayashi

Affiliation: Centro de Matemática, Computação e Cognição, Universidade Federal do ABC (UFABC), 9.210-580, Santo André, São Paulo and Brazil

Keyword(s): Artificial Intelligence, Automatic Grading, Text Classification, Deep Learning.

Related Ontology Subjects/Areas/Topics: Blended Learning ; Computer-Supported Education ; Learning/Teaching Methodologies and Assessment

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 solution s 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.62.124

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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; ISSN 2184-5026, SciTePress, pages 62-69. DOI: 10.5220/0007711000620069

@conference{csedu19,
author={Fábio De {Rezende Souza}. and Francisco de Assis 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},
issn={2184-5026},
}

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
IS - 2184-5026
AU - Rezende Souza, F.
AU - Zampirolli, F.
AU - Kobayashi, G.
PY - 2019
SP - 62
EP - 69
DO - 10.5220/0007711000620069
PB - SciTePress