Theoretical Computer Science in Basic Education: A Systematic Review
Resumo
Este artigo apresenta uma revisão sisemática da literatura sobre informática teórica na educação básica. A educação em computação foi reacendida com a nova onda do pensamento computacional, clamando pela importância de habilidades para a resolução de problemas. No entanto, seus esforços têm se concentrado em programação com linguagens visuais, jogos e robótica. Enquanto isso, informática teórica tem sido raramente abordada na educação. Por isso, este trabalho investiga 17 artigos de periódico e de conferências que apresentam soluções incluindo este tópico na educação básica nos últimos 20 anos. Para concluir, é realizada uma discussão sobre os tópicos da informática teórica utilizados e indentificada uma demanda por mais pesquisas na área.
Palavras-chave:
Theoretical Computer Science, K-12, Education, Computing Education, Theorethical Computing
Referências
Angeli, C. and Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105:106185.
Benacka, J. and Reichel, J. (2013). Computer modeling with delphi. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, pages 138–146. Springer.
Carruthers, S., Milford, T., Pelton, T., and Stege, U. (2010). Moving k-7 education into the information age. In Proceedings of the 15th Western Canadian Conference on Computing Education, pages 1–5.
Chuda, D. (2007). Visualization in education of theoretical computer science. In Proceedings of the 2007 international conference on Computer systems and technologies, pages 1–6.
Crick, T. (2017). Computing education: An overview of research in the field. London: Royal Society.
del Vado Vírseda, R. (2019a). Computability and algorithmic complexity questions in secondary education. In Proceedings of the ACM Conference on Global Computing Education, pages 51–57.
del Vado Vírseda, R. (2019b). Introducing theoretical computer concepts in secondary education. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education, pages 1272–1272.
del Vado Vírseda, R. (2020). From the mathematical impossibility results of the high school curriculum to theoretical computer science. In Koli Calling’20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research, pages 1–5.
D’Antoni, L., Helfrich, M., Kretinsky, J., Ramneantu, E., and Weininger, M. (2020). Automata tutor v3. In International Conference on Computer Aided Verification, pages 3–14. Springer.
Gari, M. R. N., Walia, G. S., and Radermacher, A. D. (2018). Gamification in computer science education: A systematic literature review. In 2018 ASEE Annual Conference & Exposition.
Garneli, V., Giannakos, M. N., and Chorianopoulos, K. (2015). Computing education in k-12 schools: A review of the literature. In 2015 IEEE Global Engineering Education Conference (EDUCON), pages 543–551.
Isayama, D., Ishiyama, M., Relator, R., and Yamazaki, K. (2016). Computer science education for primary and lower secondary school students: Teaching the concept of automata. ACM Transactions on Computing Education (TOCE), 17(1):1–28.
Kelemen, J. and Kubík, A. (2002). Robots and agents in basic computer science curricula. In Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo’02., pages 309–317. IEEE.
Kiesmüller, U. (2009). Diagnosing learners’ problem-solving strategies using learning environments with algorithmic problems in secondary education. ACM Transactions on Computing Education (TOCE), 9(3):1–26.
Knobelsdorf, M., Kreitz, C., and B”ohne, S. (2014). Teaching theoretical computer science using a cognitive apprenticeship approach. In Proceedings of the 45th ACM technical symposium on Computer science education, pages 67–72.
Kolikant, Y. B.-D. (2004). Learning concurrency: evolution of students’ understanding of synchronization. International Journal of Human-Computer Studies, 60(2):243–268.
Korte, L., Anderson, S., Pain, H., and Good, J. (2007). Learning by game-building: a novel approach to theoretical computer science education. In Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education, pages 53–57.
Levitin, A. (2016). Algorithm design strategies in cs curricula 2013: hits and misses. Journal of Computing Sciences in Colleges, 31(3):78–84.
Maxville, V. and Sandford, B. (2020). Computational science vs. zombies. In International Conference on Computational Science, pages 622–633. Springer.
Morisse, K. (2015). Implementation of the inverted classroom model for theoretical computer science. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pages 426–435. Association for the Advancement of Computing in Education (AACE).
Pantsar, M. (2021). Cognitive and computational complexity: Considerations from mathematical problem solving. Erkenntnis, 86(4):961–997.
Schlueter, K. and Brinda, T. (2008). Characteristics and dimensions of a competence model of theoretical computer science in secondary education. ACM SIGCSE Bulletin, 40(3):367–367.
Sengupta, P. and Farris, A. V. (2012). Learning kinematics in elementary grades using agent-based computational modeling: a visual programming-based approach. In Proceedings of the 11th international conference on interaction design and children, pages 78–87.
Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., and Clark, D. (2013). Integrating computational thinking with k-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2):351–380.
Sorva, J. (2007). Notional machines and introductory programming education. Trans. Comput. Educ, 13(2):1–31.
Valente, A. (2004). Exploring theoretical computer science using paper toys (for kids). In IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings., pages 301–305. IEEE.
Weintrop, D. (2019). Block-based programming in computer science education. Communications of the ACM, 62(8):22–25.
Wolf, A., Wilhelm-Weidner, A., and Nestmann, U. (2018). A case study of flipped classroom for automata theory in secondary education. In Proceedings of the 13th Workshop in Primary and Secondary Computing Education, pages 1–6.
Yesharim, M. F. and Ben-Ari, M. (2018). Teaching computer science concepts through robotics to elementary school children. International Journal of Computer Science Education in Schools, 2(3).
Benacka, J. and Reichel, J. (2013). Computer modeling with delphi. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, pages 138–146. Springer.
Carruthers, S., Milford, T., Pelton, T., and Stege, U. (2010). Moving k-7 education into the information age. In Proceedings of the 15th Western Canadian Conference on Computing Education, pages 1–5.
Chuda, D. (2007). Visualization in education of theoretical computer science. In Proceedings of the 2007 international conference on Computer systems and technologies, pages 1–6.
Crick, T. (2017). Computing education: An overview of research in the field. London: Royal Society.
del Vado Vírseda, R. (2019a). Computability and algorithmic complexity questions in secondary education. In Proceedings of the ACM Conference on Global Computing Education, pages 51–57.
del Vado Vírseda, R. (2019b). Introducing theoretical computer concepts in secondary education. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education, pages 1272–1272.
del Vado Vírseda, R. (2020). From the mathematical impossibility results of the high school curriculum to theoretical computer science. In Koli Calling’20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research, pages 1–5.
D’Antoni, L., Helfrich, M., Kretinsky, J., Ramneantu, E., and Weininger, M. (2020). Automata tutor v3. In International Conference on Computer Aided Verification, pages 3–14. Springer.
Gari, M. R. N., Walia, G. S., and Radermacher, A. D. (2018). Gamification in computer science education: A systematic literature review. In 2018 ASEE Annual Conference & Exposition.
Garneli, V., Giannakos, M. N., and Chorianopoulos, K. (2015). Computing education in k-12 schools: A review of the literature. In 2015 IEEE Global Engineering Education Conference (EDUCON), pages 543–551.
Isayama, D., Ishiyama, M., Relator, R., and Yamazaki, K. (2016). Computer science education for primary and lower secondary school students: Teaching the concept of automata. ACM Transactions on Computing Education (TOCE), 17(1):1–28.
Kelemen, J. and Kubík, A. (2002). Robots and agents in basic computer science curricula. In Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo’02., pages 309–317. IEEE.
Kiesmüller, U. (2009). Diagnosing learners’ problem-solving strategies using learning environments with algorithmic problems in secondary education. ACM Transactions on Computing Education (TOCE), 9(3):1–26.
Knobelsdorf, M., Kreitz, C., and B”ohne, S. (2014). Teaching theoretical computer science using a cognitive apprenticeship approach. In Proceedings of the 45th ACM technical symposium on Computer science education, pages 67–72.
Kolikant, Y. B.-D. (2004). Learning concurrency: evolution of students’ understanding of synchronization. International Journal of Human-Computer Studies, 60(2):243–268.
Korte, L., Anderson, S., Pain, H., and Good, J. (2007). Learning by game-building: a novel approach to theoretical computer science education. In Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education, pages 53–57.
Levitin, A. (2016). Algorithm design strategies in cs curricula 2013: hits and misses. Journal of Computing Sciences in Colleges, 31(3):78–84.
Maxville, V. and Sandford, B. (2020). Computational science vs. zombies. In International Conference on Computational Science, pages 622–633. Springer.
Morisse, K. (2015). Implementation of the inverted classroom model for theoretical computer science. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pages 426–435. Association for the Advancement of Computing in Education (AACE).
Pantsar, M. (2021). Cognitive and computational complexity: Considerations from mathematical problem solving. Erkenntnis, 86(4):961–997.
Schlueter, K. and Brinda, T. (2008). Characteristics and dimensions of a competence model of theoretical computer science in secondary education. ACM SIGCSE Bulletin, 40(3):367–367.
Sengupta, P. and Farris, A. V. (2012). Learning kinematics in elementary grades using agent-based computational modeling: a visual programming-based approach. In Proceedings of the 11th international conference on interaction design and children, pages 78–87.
Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., and Clark, D. (2013). Integrating computational thinking with k-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2):351–380.
Sorva, J. (2007). Notional machines and introductory programming education. Trans. Comput. Educ, 13(2):1–31.
Valente, A. (2004). Exploring theoretical computer science using paper toys (for kids). In IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings., pages 301–305. IEEE.
Weintrop, D. (2019). Block-based programming in computer science education. Communications of the ACM, 62(8):22–25.
Wolf, A., Wilhelm-Weidner, A., and Nestmann, U. (2018). A case study of flipped classroom for automata theory in secondary education. In Proceedings of the 13th Workshop in Primary and Secondary Computing Education, pages 1–6.
Yesharim, M. F. and Ben-Ari, M. (2018). Teaching computer science concepts through robotics to elementary school children. International Journal of Computer Science Education in Schools, 2(3).
Publicado
17/11/2021
Como Citar
S. JUNIOR, Braz A.; CAVALHEIRO, Simone A. C.; FOSS, Luciana.
Theoretical Computer Science in Basic Education: A Systematic Review. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 6. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 133-140.
DOI: https://doi.org/10.5753/weit.2021.18933.
