Além da Corretude: Investigando os Limites da Correção Automática ao Analisar Códigos Corretos
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Hollingsworth, J. (1960). Automatic graders for programming classes. Commun. ACM, 3(10):528–529.
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Kaczmarczyk, L. C., Petrick, E. R., East, J. P., and Herman, G. L. (2010). Identifying student misconceptions of programming. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE ’10, page 107–111, New York, NY, USA. Association for Computing Machinery.
Keuning, H., Heeren, B., and Jeuring, J. (2021). A tutoring system to learn code refactoring. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, SIGCSE ’21, page 562–568, New York, NY, USA. Association for Computing Machinery.
Lancaster, T., Robins, A. V., and Fincher, S. A. (2019). Assessment and Plagiarism, page 414–444. Cambridge Handbooks in Psychology. Cambridge University Press.
Prather, J., Pettit, R., McMurry, K., Peters, A., Homer, J., and Cohen, M. (2018). Metacognitive difficulties faced by novice programmers in automated assessment tools. ICER 2018 - Proceedings of the 2018 ACM Conference on International Computing Education Research, pages 41–50.
Qian, Y. and Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education (TOCE)., 18(1).
Robins, A. V. (2022). Dual process theories: Computing cognition in context. ACM Trans. Comput. Educ., 22(4).
Silva, E. (2024). Misconceptions in Correct Code: Assisting Instructors and Students by Shedding Light on What Is Potentially Overshadowed by Automated Correction. Tese (doutorado), Universidade Estadual de Campinas (UNICAMP), Instituto de Computação, Campinas, SP, Brazil. Disponível em: [link]. Acesso em: 21 mar. 2025.
Silva, E., Caceffo, R., and Azevedo, R. (2021). Análise estática de código em conjunto com autograders. In Anais Estendidos do I Simpósio Brasileiro de Educação em Computação, pages 25–26, Porto Alegre, RS, Brasil. SBC.
Silva, E., Caceffo, R., and Azevedo, R. (2023a). Misconceptions in Correct Code: rating the severity of undesirable programming behaviors in Python CS1 courses. Technical Report IC-23-01, Institute of Computing, University of Campinas.
Silva, E., Caceffo, R., and Azevedo, R. (2023b). A syllabi analysis of cs1 courses from brazilian public universities. Brazilian Journal of Computers in Education, 31(1):407–436.
Walker, H. M. (2017). ACM RETENTION COMMITTEE Retention of Students in Introductory Computing Courses: Curricular Issues and Approaches. ACM Inroads, 8(4):14–16.
Wigfield, A. and Eccles, J. S. (2000). Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1):68–81.
Becker, B. A. and Fitzpatrick, T. (2019). What do cs1 syllabi reveal about our expectations of introductory programming students? In Proceedings of the 50th ACM Technical Symposium on Computer Science Education, SIGCSE ’19, page 1011–1017, New York, NY, USA. Association for Computing Machinery.
Black, P. and William, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1):7–74.
Blikstein, P. and Moghadam, S. H. (2019). Computing Education Literature Review and Voices from the Field, page 56–78. Cambridge Handbooks in Psychology. Cambridge University Press.
Bloom, B., Hastings, J., and Madaus, G. (1971). Handbook on Formative and Summative Evaluation of Student Learning. McGraw-Hill.
Bosse, Y. and Gerosa, M. (2015). Reprovações e Trancamentos nas Disciplinas de Introdução à Programação da Universidade de São Paulo: Um Estudo Preliminar. In Anais do XXIII Workshop sobre Educação em Computação, pages 426–435, Porto Alegre, RS, Brasil. SBC.
Caceffo, R., Wolfman, S., Booth, K. S., and Azevedo, R. (2016). Developing a Computer Science Concept Inventory for Introductory Programming. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education, SIGCSE ’16, page 364–369, New York, NY, USA. Association for Computing Machinery.
De Ruvo, G., Tempero, E., Luxton-Reilly, A., Rowe, G. B., and Giacaman, N. (2018). Understanding semantic style by analysing student code. In Proceedings of the 20th Australasian Computing Education Conference, ACE ’18, page 73–82, New York, NY, USA. Association for Computing Machinery.
Guzdial, M. and du Boulay, B. (2019). The History of Computing Education Research, page 11–39. Cambridge Handbooks in Psychology. Cambridge University Press.
Hertz, M. (2010). What Do "CS1"and "CS2"Mean? Investigating Differences in the Early Courses. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE ’10, page 199–203, New York, NY, USA. Association for Computing Machinery.
Hollingsworth, J. (1960). Automatic graders for programming classes. Commun. ACM, 3(10):528–529.
Ihantola, P. and Petersen, A. (2019). Code complexity in introductory programming courses. In Proceedings of the 52nd Hawaii International Conference on System Sciences, pages 7662–7670.
Kaczmarczyk, L. C., Petrick, E. R., East, J. P., and Herman, G. L. (2010). Identifying student misconceptions of programming. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE ’10, page 107–111, New York, NY, USA. Association for Computing Machinery.
Keuning, H., Heeren, B., and Jeuring, J. (2021). A tutoring system to learn code refactoring. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, SIGCSE ’21, page 562–568, New York, NY, USA. Association for Computing Machinery.
Lancaster, T., Robins, A. V., and Fincher, S. A. (2019). Assessment and Plagiarism, page 414–444. Cambridge Handbooks in Psychology. Cambridge University Press.
Prather, J., Pettit, R., McMurry, K., Peters, A., Homer, J., and Cohen, M. (2018). Metacognitive difficulties faced by novice programmers in automated assessment tools. ICER 2018 - Proceedings of the 2018 ACM Conference on International Computing Education Research, pages 41–50.
Qian, Y. and Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education (TOCE)., 18(1).
Robins, A. V. (2022). Dual process theories: Computing cognition in context. ACM Trans. Comput. Educ., 22(4).
Silva, E. (2024). Misconceptions in Correct Code: Assisting Instructors and Students by Shedding Light on What Is Potentially Overshadowed by Automated Correction. Tese (doutorado), Universidade Estadual de Campinas (UNICAMP), Instituto de Computação, Campinas, SP, Brazil. Disponível em: [link]. Acesso em: 21 mar. 2025.
Silva, E., Caceffo, R., and Azevedo, R. (2021). Análise estática de código em conjunto com autograders. In Anais Estendidos do I Simpósio Brasileiro de Educação em Computação, pages 25–26, Porto Alegre, RS, Brasil. SBC.
Silva, E., Caceffo, R., and Azevedo, R. (2023a). Misconceptions in Correct Code: rating the severity of undesirable programming behaviors in Python CS1 courses. Technical Report IC-23-01, Institute of Computing, University of Campinas.
Silva, E., Caceffo, R., and Azevedo, R. (2023b). A syllabi analysis of cs1 courses from brazilian public universities. Brazilian Journal of Computers in Education, 31(1):407–436.
Walker, H. M. (2017). ACM RETENTION COMMITTEE Retention of Students in Introductory Computing Courses: Curricular Issues and Approaches. ACM Inroads, 8(4):14–16.
Wigfield, A. and Eccles, J. S. (2000). Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1):68–81.
Publicado
07/04/2025
Como Citar
SILVA, Eryck Pedro da; CACEFFO, Ricardo; AZEVEDO, Rodolfo.
Além da Corretude: Investigando os Limites da Correção Automática ao Analisar Códigos Corretos. In: CONCURSO DE TESES E DISSERTAÇÕES EM EDUCAÇÃO EM COMPUTAÇÃO - SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 91-95.
ISSN 3086-0741.
DOI: https://doi.org/10.5753/educomp_estendido.2025.6524.
