Um Framework de Estratégias de Autorregulação Contextualizado em Programação Introdutória
Resumo
A aprendizagem autorregulada é definida como o grau em que os alunos são participantes ativos em sua aprendizagem quanto aos aspectos motivacional, comportamental, metacognitivo e cognitivo. Pesquisas recentes mostram, que a maioria dos novatos tem habilidades de autorregulação pobres, que estão associadas a resultados ruins em programação. Deste modo, neste trabalho é aplicado um método exploratório da literatura para contribuir com uma taxonomia de estratégias regulatórias, a fim de apoiar os professores de programação introdutória, na criação de cenários de aprendizagem para promover a autorregulação dos alunos.
Referências
Arab, M., Liang, J., Yoo, Y., Ko, A. J., and LaToza, T. D. (2021). HowToo: A Platform for Sharing, Finding, and Using Programming Strategies. IEEE. ISBN 978-1-6654-4592-4.
Bellhäuser, H., Lösch, T., Winter, C., and Schmitz, B. (2016). Applying a web-based training to foster self-regulated learning — Effects of an intervention for large numbers of participants. The Internet and Higher Education, 31, 87-100. https://doi.org/10.1016/j.iheduc.2016.07.002.
Bergin, S., Reilly, R., and Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. In Proceedings of the first international workshop on Computing education research (pp. 81-86). ACM.
Caruso, T., Hill, N., Van DeGrift, T., & Simon, B. (2011). Experience report: Getting novice programmers to THINK about improving their software development process. Paper presented at the SIGCSE'11 Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, 493-498. doi:10.1145/1953163.1953307
Chang, B. (2019). Reflection in learning. Online Learning, 23(1), 95-110. doi:10.24059/olj.v23i1.1447
Daradoumis, T., Marquès Puig, J. M., Arguedas, M., et al. (2021). A distributed systems laboratory that helps students accomplish their assignments through self-regulation of behavior. Education Tech Research Dev, 69(4), 1077-1099. doi:10.1007/s11423-021-09975-6
Falkner, K., Vivian, R., and Falkner, N. J. G. (2014). Identifying computer science self-regulated learning strategies. In Proceedings of the 2014 conference on Innovation and technology in computer science education (pp. 291-296). ACM. doi:10.1145/2591708.2591715
Gibbons, A. S., and Bunderson, C. V. (2005). Explore, explain, design. In K. K. Leondard (Ed.), Encyclopedia of Social Measurement (pp. 927-938). New York, NY: Elsevier.
Guilford, J. P. (1950). Creativity. American Psychologist, 5, pp. 444–454. Hauswirth, M., and Adamoli, A. (2017). Metacognitive calibration when learning to program. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research (pp. 50-59). ACM.
Johnson, W. L. and Soloway, E. (1984). Proust: Knowledge-based program understanding. In Proceedings of the 7th international conference on Software engineering, Florida, United States, pages 369 – 380.
Keller, J. M. (2017). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1-13. doi:10.17275/per.16.06.3.2 Kimmons, R., Graham, C. R., and West, R. E. (2020). The PICRAT model for technology integration in teacher preparation. Contemporary Issues in Technology and Teacher Education, 20(1). doi:10.7916/cite.v20i1.4486
Law, K. M. Y., Lee, V. C. S., and Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers and Education, 55(1), 218-228. doi:10.1016/j.compedu.2010.01.007
Loksa, D. (2020). Explicitly training metacognition and self-regulation for computer programming [Doctoral dissertation, University of Washington].
Loksa, D., and Ko, A. J. (2016). The role of self-regulation in programming problem-solving process and success. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER '16) (pp. 83-91). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2960310.2960334.
Malmi, L., Sheard, J., Kinnunen, P., Simon, and Sinclair, J. (2019). Computing education theories: What are they and how are they used? In Proceedings of the 2019 ACM Conference on International Computing Education Research (pp. 187-197). ACM. doi:10.1145/3291279.3339409
Mourão, A.B. and Netto, J.F.M. 2019. SIMROAA Multi-Agent Recommendation System for Recommending Accessible Learning Objects. In Frontiers in Education Conference (FIE) 2019 IEEE, pp. 1-9.
Pedrosa, D., Cravino, J., Morgado, L., and Barreira, C. 2019. Co-regulated Learning in Computer Programming: Students Co-reflection About Learning Strategies Adopted During an Assignment.
Pintrich, P. 2000. The Role of Goal Orientation in Self-Regulated Learning. PhD thesis, The University of Michigan, Ann Arbor, Michigan.
Law, K.M.Y., Lee, V.C.S., and Yu, Y.T. 2010. Learning motivation in e-learning facilitated computer programming courses. Computers and Education 55, 1 (2010), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007.
Prather, J., Becker, B.A., Craig, M., Denny, P., Loksa, D., and Margulieux, L. 2020. What Do We Think We Think We Are Doing? Metacognition and Self-Regulation in Programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research (ICER '20). Association for Computing Machinery, New York, NY, USA, 2–13. https://doi.org/10.1145/3372782.3406263.
Schoeffel, P. 2019. A Method to Predict At-Risk Students in Introductory Computing Courses Based on Motivation. PhD thesis, Programa de Pós-Graduação em Ciência da Computação da Universidade Federal de Santa Catarina.
Szabo, C., Falkner, N., Petersen, A., Bort, H., Cunningham, K., Donaldson, P., Hellas, A., Robinson, J., and Sheard, J. 2019. Review and Use of Learning Theories within Computer Science Education Research: Primer for Researchers and Practitioners. In Proc. of the WG Reports on Innovation and Technology in Comp Sci Education (Aberdeen, Scotland Uk) (ITiCSE-WGR ’19). ACM, NY, USA, 89–109. https://doi.org/10.1145/3344429.3372504.
Wallingford, E. (2001). The Elementary Patterns Home Page. https://www.cs.uni.edu/~wallingf/patterns/elementary/
Bellhäuser, H., Lösch, T., Winter, C., and Schmitz, B. (2016). Applying a web-based training to foster self-regulated learning — Effects of an intervention for large numbers of participants. The Internet and Higher Education, 31, 87-100. https://doi.org/10.1016/j.iheduc.2016.07.002.
Bergin, S., Reilly, R., and Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. In Proceedings of the first international workshop on Computing education research (pp. 81-86). ACM.
Caruso, T., Hill, N., Van DeGrift, T., & Simon, B. (2011). Experience report: Getting novice programmers to THINK about improving their software development process. Paper presented at the SIGCSE'11 Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, 493-498. doi:10.1145/1953163.1953307
Chang, B. (2019). Reflection in learning. Online Learning, 23(1), 95-110. doi:10.24059/olj.v23i1.1447
Daradoumis, T., Marquès Puig, J. M., Arguedas, M., et al. (2021). A distributed systems laboratory that helps students accomplish their assignments through self-regulation of behavior. Education Tech Research Dev, 69(4), 1077-1099. doi:10.1007/s11423-021-09975-6
Falkner, K., Vivian, R., and Falkner, N. J. G. (2014). Identifying computer science self-regulated learning strategies. In Proceedings of the 2014 conference on Innovation and technology in computer science education (pp. 291-296). ACM. doi:10.1145/2591708.2591715
Gibbons, A. S., and Bunderson, C. V. (2005). Explore, explain, design. In K. K. Leondard (Ed.), Encyclopedia of Social Measurement (pp. 927-938). New York, NY: Elsevier.
Guilford, J. P. (1950). Creativity. American Psychologist, 5, pp. 444–454. Hauswirth, M., and Adamoli, A. (2017). Metacognitive calibration when learning to program. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research (pp. 50-59). ACM.
Johnson, W. L. and Soloway, E. (1984). Proust: Knowledge-based program understanding. In Proceedings of the 7th international conference on Software engineering, Florida, United States, pages 369 – 380.
Keller, J. M. (2017). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1-13. doi:10.17275/per.16.06.3.2 Kimmons, R., Graham, C. R., and West, R. E. (2020). The PICRAT model for technology integration in teacher preparation. Contemporary Issues in Technology and Teacher Education, 20(1). doi:10.7916/cite.v20i1.4486
Law, K. M. Y., Lee, V. C. S., and Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers and Education, 55(1), 218-228. doi:10.1016/j.compedu.2010.01.007
Loksa, D. (2020). Explicitly training metacognition and self-regulation for computer programming [Doctoral dissertation, University of Washington].
Loksa, D., and Ko, A. J. (2016). The role of self-regulation in programming problem-solving process and success. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER '16) (pp. 83-91). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2960310.2960334.
Malmi, L., Sheard, J., Kinnunen, P., Simon, and Sinclair, J. (2019). Computing education theories: What are they and how are they used? In Proceedings of the 2019 ACM Conference on International Computing Education Research (pp. 187-197). ACM. doi:10.1145/3291279.3339409
Mourão, A.B. and Netto, J.F.M. 2019. SIMROAA Multi-Agent Recommendation System for Recommending Accessible Learning Objects. In Frontiers in Education Conference (FIE) 2019 IEEE, pp. 1-9.
Pedrosa, D., Cravino, J., Morgado, L., and Barreira, C. 2019. Co-regulated Learning in Computer Programming: Students Co-reflection About Learning Strategies Adopted During an Assignment.
Pintrich, P. 2000. The Role of Goal Orientation in Self-Regulated Learning. PhD thesis, The University of Michigan, Ann Arbor, Michigan.
Law, K.M.Y., Lee, V.C.S., and Yu, Y.T. 2010. Learning motivation in e-learning facilitated computer programming courses. Computers and Education 55, 1 (2010), 218-228. https://doi.org/10.1016/j.compedu.2010.01.007.
Prather, J., Becker, B.A., Craig, M., Denny, P., Loksa, D., and Margulieux, L. 2020. What Do We Think We Think We Are Doing? Metacognition and Self-Regulation in Programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research (ICER '20). Association for Computing Machinery, New York, NY, USA, 2–13. https://doi.org/10.1145/3372782.3406263.
Schoeffel, P. 2019. A Method to Predict At-Risk Students in Introductory Computing Courses Based on Motivation. PhD thesis, Programa de Pós-Graduação em Ciência da Computação da Universidade Federal de Santa Catarina.
Szabo, C., Falkner, N., Petersen, A., Bort, H., Cunningham, K., Donaldson, P., Hellas, A., Robinson, J., and Sheard, J. 2019. Review and Use of Learning Theories within Computer Science Education Research: Primer for Researchers and Practitioners. In Proc. of the WG Reports on Innovation and Technology in Comp Sci Education (Aberdeen, Scotland Uk) (ITiCSE-WGR ’19). ACM, NY, USA, 89–109. https://doi.org/10.1145/3344429.3372504.
Wallingford, E. (2001). The Elementary Patterns Home Page. https://www.cs.uni.edu/~wallingf/patterns/elementary/
Publicado
06/08/2023
Como Citar
FERREIRA, Deller James; CAMPOS, Dirson Santos de; GONÇALVES, Anderson Cavalcante.
Um Framework de Estratégias de Autorregulação Contextualizado em Programação Introdutória. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 31. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 396-407.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2023.229714.
