Proposta para Avaliação de Códigos Fonte com TF-IDF
Resumo
A crescente demanda de profissionais capazes de elaborar e manter softwares acarreta tambem no aumento de cursos voltados ao ensino de programação. O presente trabalho propõe um modelo de avaliação de códigos fonte produzidos em cursos de programação. O modelo é baseado em TF-IDF para identificar e estimar habilidades do pensamento computacional em códigos fonte. Os resultados demostram ser uma abordagem de avaliação promissora na comparação de habilidades em diferentes fontes.
Palavras-chave:
avaliação de códigos fonte, TF-IDF, ensino programação
Referências
ALA-MUTKA, K. M. (2005). A survey of automated assessment approaches for programming assignments. Computer science education, 15(2):83–102.
AZCONA, D., Arora, P., Hsiao, I.-H., and Smeaton, A. (2019). user2code2vec: Embeddings for profiling students based on distributional representations of source code. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge,
pages 86–95.
BARR, V. and STEPHENSON, C. (2011). Bringing computational thinking to k-12: what is involved and what is the role of the computer science education community? Acm Inroads, 2(1):48–54.
CABO, C. (2019). Student progress in learning computer programming: Insights from association analysis. In 2019 IEEE Frontiers in Education Conference (FIE), pages
1–8. IEEE.
CAPITAN, L. and VOGEL-HEUSER, B. (2017). Metrics for software quality in automated production systems as an indicator for technical debt. In 2017 13th IEEE
Conference on Automation Science and Engineering (CASE), pages 709–716. IEEE.
GANGULY, D., Jones, G. J., Ram´ırez-De-La-Cruz, A., Ram´ırez-De-La-Rosa, G., and Villatoro-Tello, E. (2018). Retrieving and classifying instances of source code plagiarism. Information Retrieval Journal, 21(1):1–23.
KARNALIM, O. (2020). Tf-idf inspired detection for cross-language source code plagiarism and collusion. Computer Science, 21(1).
MOREIRA, M. P. and FAVERO, E. L. (2009). Um ambiente para ensino de programação com feedback automatico de exercícios. Workshop sobre Educação em Computação (WEI 2009).
RAIGOZA, J. (2017). A study of students’ progress through introductory computer science programming courses. In 2017 IEEE Frontiers in Education Conference (FIE), pages 1–7. IEEE.
ROBINS, A. (2010). Learning edge momentum: A new account of outcomes in cs1. Computer Science Education, 20(1):37–71.
SOUZA, D., FELIZARDO, k., and BARBOSA, E. (2016). A systematic literature review of assessment tools for programming assingnments. In 2016 IEEE 29th international Conference on Software Engeneering Education and Training (CSEET), pages 147–156. IEEE.
SU, X., Qiu, J., Wang, T., and Zhao, L. (2016). Optimization and improvements of a moodle-based online learning system for c programming. In 2016 IEEE Frontiers in
Education Conference (FIE), pages 1–8. IEEE.
ULLAH, F., Wang, J., Farhan, M., Jabbar, S., Wu, Z., and Khalid, S. (2018a). Plagiarism detection in students’ programming assignments based on semantics: multimedia elearning based smart assessment methodology. Multimedia Tools and Applications, pages 1–18.
ULLAH, Z., Lajis, A., Jamjoom, M., Altalhi, A., Al-Ghamdi, A., and Saleem, F. (2018b). The effect of automatic assessment on novice programming: Strengths and limitations of existing systems. Computer Applications in Engineering Education, 26(6):2328–2341.
WING, J. M. (2006). Computational thinking. Communications of the ACM, 49(3):33–35.
AZCONA, D., Arora, P., Hsiao, I.-H., and Smeaton, A. (2019). user2code2vec: Embeddings for profiling students based on distributional representations of source code. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge,
pages 86–95.
BARR, V. and STEPHENSON, C. (2011). Bringing computational thinking to k-12: what is involved and what is the role of the computer science education community? Acm Inroads, 2(1):48–54.
CABO, C. (2019). Student progress in learning computer programming: Insights from association analysis. In 2019 IEEE Frontiers in Education Conference (FIE), pages
1–8. IEEE.
CAPITAN, L. and VOGEL-HEUSER, B. (2017). Metrics for software quality in automated production systems as an indicator for technical debt. In 2017 13th IEEE
Conference on Automation Science and Engineering (CASE), pages 709–716. IEEE.
GANGULY, D., Jones, G. J., Ram´ırez-De-La-Cruz, A., Ram´ırez-De-La-Rosa, G., and Villatoro-Tello, E. (2018). Retrieving and classifying instances of source code plagiarism. Information Retrieval Journal, 21(1):1–23.
KARNALIM, O. (2020). Tf-idf inspired detection for cross-language source code plagiarism and collusion. Computer Science, 21(1).
MOREIRA, M. P. and FAVERO, E. L. (2009). Um ambiente para ensino de programação com feedback automatico de exercícios. Workshop sobre Educação em Computação (WEI 2009).
RAIGOZA, J. (2017). A study of students’ progress through introductory computer science programming courses. In 2017 IEEE Frontiers in Education Conference (FIE), pages 1–7. IEEE.
ROBINS, A. (2010). Learning edge momentum: A new account of outcomes in cs1. Computer Science Education, 20(1):37–71.
SOUZA, D., FELIZARDO, k., and BARBOSA, E. (2016). A systematic literature review of assessment tools for programming assingnments. In 2016 IEEE 29th international Conference on Software Engeneering Education and Training (CSEET), pages 147–156. IEEE.
SU, X., Qiu, J., Wang, T., and Zhao, L. (2016). Optimization and improvements of a moodle-based online learning system for c programming. In 2016 IEEE Frontiers in
Education Conference (FIE), pages 1–8. IEEE.
ULLAH, F., Wang, J., Farhan, M., Jabbar, S., Wu, Z., and Khalid, S. (2018a). Plagiarism detection in students’ programming assignments based on semantics: multimedia elearning based smart assessment methodology. Multimedia Tools and Applications, pages 1–18.
ULLAH, Z., Lajis, A., Jamjoom, M., Altalhi, A., Al-Ghamdi, A., and Saleem, F. (2018b). The effect of automatic assessment on novice programming: Strengths and limitations of existing systems. Computer Applications in Engineering Education, 26(6):2328–2341.
WING, J. M. (2006). Computational thinking. Communications of the ACM, 49(3):33–35.
Publicado
24/11/2020
Como Citar
SOUZA, Ricardo Lemos de; FERREIRA, Fabiana Zaffalon; BOTELHO, Silvia Silva da Costa.
Proposta para Avaliação de Códigos Fonte com TF-IDF. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 112-121.
DOI: https://doi.org/10.5753/cbie.sbie.2020.112.
