Trends in Developing Computational Thinking: Perspectives from a Mathematics Teacher Education Course

Framework

This research is grounded in the Historical-Cultural Theory, adopting a specific perspective on Computational Thinking (CT) in Mathematics Education geared towards overcoming purely technical definitions. We adopt the concept developed by Navarro (2021), which views CT not as a technique or mere software usage, but as an inseparable concatenation of three dialectical and interdependent conceptual links: problem-solving, algebraic thinking, and algorithmic thinking. Problem-solving is understood as a human activity that drives intellectual development. Algebraic thinking focuses on the ability to generalize patterns and systematize thought. Algorithmic thinking refers to the logical organization of finite steps to solve classes of problems.
Furthermore, regarding teacher training, the study utilizes the TPACK model (Technological Pedagogical Content Knowledge) by Mishra and Koehler (2006) as a theoretical lens. This framework supports the integration of Content Knowledge (CK), Pedagogical Knowledge (PK), and Technological Knowledge (TK). The research also incorporates perspectives on "Unplugged Computing" (Navarro, 2021; Brackmann, 2017) to democratize access to CT , and the critical integration of Generative Artificial Intelligence (UNESCO, 2024) as a support for pedagogical planning.

Methods

This study adopts a qualitative approach, utilizing a case study method to examine the phenomenon of continuing teacher education within its real-life context. The "case" is delimited by a group of teachers participating in an extension course titled "Computational Thinking for Teaching Mathematics," offered in a MOOC (Massive Open Online Course) format with a workload of 20 hours distributed across four modules.
Participants: Initially, 200 teachers enrolled, with 69 effectively beginning the activities (completing the diagnostic instrument) and 39 participants completing all stages (completing the final instrument). The group consisted mainly of Mathematics teachers from Basic Education and undergraduate students from various regions of Brazil.
Data Sources: Data was collected using two structured questionnaires administered via Google Forms:
Initial Questionnaire (Diagnosis): Mapped the sociodemographic profile, prior knowledge of CT (self-assessment scale 1-5), and initial definitions of the relationship between CT and Mathematics.
Final Questionnaire (Trend Analysis): Explored conceptual resignifications, trends in tool usage (plugged/unplugged/AI), and pedagogical security. It included open-ended questions asking for "WOW moments" and intentions for future application.
Analysis: The data were interpreted using Content Analysis proposed by Bardin (2011), following three phases: pre-analysis, exploration of the material (coding/categorization), and processing/interpretation of results.

Results

The analysis revealed significant shifts in teachers' conceptions of Computational Thinking (CT). Initially, participants held a technocratic or generalist view, often confusing CT with digital literacy or hardware operation. Post-intervention, the results indicated a qualitative evolution towards understanding CT through conceptual links (problem-solving, algebraic, and algorithmic thinking), viewing it as a thought process rather than just technology use.
Key results include:
Unplugged Computing: A major finding was the high valuation of unplugged activities. Participants identified the realization that CT can be taught without computers as a significant insight ("WOW moment"), increasing their self-efficacy and confidence to teach CT in schools with limited infrastructure.

Artificial Intelligence: There was a strong trend toward using Generative AI (ChatGPT, Gemini, Copilot) for lesson planning. While 78.9% of graduates intend to use ChatGPT and 76.3% intend to use Gemini, they view these tools not as replacements but as assistants for creating prompts and activities, subject to critical validation by the teacher.
Pedagogical Impact: At the end of the course, 97.3% of participants stated the training brought new possibilities to their practice, and all respondents indicated it was "very likely" they would conduct classes intentionally developing CT.

Importance

This study addresses a critical gap in teacher training: while national curricula (like the Brazilian BNCC) mandate the inclusion of Computational Thinking (CT) in Mathematics, many teachers lack prior contact with these concepts during their initial training. This research is scientifically important as it validates that MOOCs can effectively move teachers from a "technocratic" view to a "conceptual" understanding of CT when grounded in theoretical frameworks like Navarro’s links.
Educationally, the study highlights the value of "Unplugged Computing" as a strategy for equity, demonstrating that low-tech approaches can effectively mobilize high-level cognitive processes (abstraction, decomposition) and democratize access to CT in resource-constrained environments. Furthermore, it contributes to the emerging field of AI in education by demonstrating how teachers can be trained to use Generative AI critically as a planning partner, reinforcing the teacher's role as a curator and mediator.

References

BARCELOS, T. S.; BORTOLETTO, R.; ANDRIOLI, M. G. Formação online para o desenvolvimento do Pensamento Computacional em professores de Matemática. In: Anais do V Congresso Brasileiro de Informática na Educação. Uberlândia: Sociedade Brasileira de Computação, 2016. p. 1228-1237.

BARDIN, L. Análise de conteúdo. São Paulo: Edições 70, 2011.

BELL, T.; WITTEN, I. H.; FELLOWS, M. Computer Science Unplugged: Ensinando Ciência da Computação sem o uso do computador. Tradução de Luciano Porto Barreto. 2011.

BOGDAN, R. C.; BIKLEN, S. K. Investigação qualitativa em educação: uma introdução à teoria e aos métodos. Porto: Porto Editora, 1994.

BRACKMANN, C. P. Desenvolvimento do Pensamento Computacional através de atividades desplugadas na Educação Básica. 2017. Tese (Doutorado em Informática na Educação) – Universidade Federal do Rio Grande do Sul, Porto Alegre, 2017.

BRASIL. Ministério da Educação. Base Nacional Comum Curricular. Brasília, 2018.

BRASIL. Conselho Nacional de Educação. Parecer CNE/CEB nº 2/2022: Normas sobre Computação na Educação Básica – Complemento à BNCC. Brasília, 2022.

BRITO, G. da S.; MENTA, E. O papel da Inteligência Artificial no Ensino Tecnológico: Implicações emergentes. Educitec - Revista de Estudos e Pesquisas sobre Ensino Tecnológico, v. 10, e232524, 2024.

BULCÃO, J. S. B.; MADEIRA, C. A. G.; GUIMARÃES, C. A.; ALVES, C. Formação Continuada de Professores em Pensamento Computacional: Um Relato de Experiência do Programa Norte-rio-grandense de Pensamento Computacional. In: Anais do I Simpósio Brasileiro de Educação em Computação (EduComp). Jataí: SBC, 2021. p. 219-226.

DAPOZO, G. et al. Capacitación en programación para incorporar el pensamiento computacional en las escuelas. Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología, n. 18, p. 113-121, 2016.

GIL, A. C. Métodos e técnicas da pesquisa social. 6. ed. São Paulo: Atlas, 2008.

GIL, A. C. Estudo de caso: Fundamentação científica, subsídios para coleta e análise de dados e como redigir o relatório. São Paulo: Atlas, 2009.

JUNIOR, L. J. da C., JUCÁ, R. de S., NETO, A. J. de. B. (2024). O Pensamento Computacional na formação inicial e continuada do professor de Matemática. Revista Edutec - Educação, Tecnologias Digitais E Formação Docente, 4(1). https://doi.org/10.55028/edutec.v4i1.19584

MAGNAVITA, L. J. A. Educação a Distância e Formação de Professores. Revista da FAEEBA – Educação e Contemporaneidade, v. 12, n. 20, p. 331–342, 2003.

MIKUSKA, M. I. S.; PRADO, M. E. B. B.; VALENTE, J. A. Formação de Professores no Brasil em Pensamento Computacional: uma Revisão Sistemática de Literatura. Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología, n. 38, p. 40-51, 2024.

MISHRA, P.; KOEHLER, M. J. Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, v. 108, n. 6, p. 1017–1054, 2006.

NAVARRO, E. R. O desenvolvimento do conceito de pensamento computacional na Educação Matemática segundo contribuições da Teoria Histórico-Cultural. 2021. Tese (Doutorado em Educação) – Universidade Federal de São Carlos, São Carlos, 2021.

RESNICK, M. et al. Scratch: programming for all. Communications of the ACM, v. 52, n. 11, p. 60-67, 2009.

RIBEIRO, A. R. A., NAVARRO, E. R., KALINKE, M. A. (2024). O uso do ChatGPT para resolver problemas matemáticos sobre grandezas direta e inversamente proporcionais. Revista Pesquisa Qualitativa, 12(30), 01–21. https://doi.org/10.33361/RPQ.2024.v.12.n.30.716

STAKE, R. E. Investigación con estudio de casos. Madrid: Morata, 1998.

UNESCO. Guia para a IA generativa na educação e na pesquisa. Paris: UNESCO, 2024.

VALENTE, J. A. Integração do pensamento computacional no currículo da educação básica: diferentes estratégias usadas e questões de formação de professores e avaliação do aluno. Revista e-Curriculum, v. 14, n. 3, p. 864-897, 2016.

VIGOTSKI, L. S. A construção do pensamento e da linguagem. São Paulo: Martins Fontes, 2000.

WING, J. M. Computational Thinking. Communications of the ACM, v. 49, n. 3, p. 33-35, 2006.

YIN, R. K. Estudo de Caso: Planejamento e métodos. 5. ed. Porto Alegre: Bookman, 2015.