Percepção dos alunos sobre o agente conversacional para inovar o processo educacional de programação Python

Autores

DOI:

https://doi.org/10.35588/s9mpgc07

Palavras-chave:

Agente conversacional, ensino, ensino superior, TIC

Resumo

O objetivo geral desta pesquisa mista é analisar a percepção dos alunos sobre a utilização do Agente Conversacional para Programação Python (ACPP) considerando a ciência de dados. A amostra é composta por 25 alunos do Bacharelado em Ciências da Terra que cursaram a disciplina Ferramentas Computacionais durante o ano letivo de 2025 na Universidade Nacional Autônoma do México. Da mesma forma, foi utilizada outra amostra composta por 5 professores do Mestrado em Docência para o Ensino Médio da UNAM. Os resultados indicam que o ACPP favorece os aspectos de aprendizagem, motivação e entusiasmo. Da mesma forma, o algoritmo da árvore de decisão criou 2 modelos de previsão considerando estilo de aprendizagem, gênero e habilidades tecnológicas. Concluindo, o ACPP representa uma alternativa tecnológica de inovação educacional porque os alunos podem se comunicar com este agente conversacional a qualquer hora do dia, independentemente da localização física.

Downloads

Os dados de download ainda não estão disponíveis.

Biografia do Autor

  • Ricardo-Adán Salas-Rueda, Escuela Nacional de Ciencias de la Tierra, Universidad Nacional Autónoma de México

    Dr., Escuela Nacional de Ciencias de la Tierra, Universidad Nacional Autónoma de México. Ciudad de México, México. 

Referências

Almarashdi, H. S., Jarrah, A. M., Abu-Khurma, O. y Gningue, S. M. (2024). Unveiling the potential: A systematic review of ChatGPT in transforming mathematics teaching and learning. Eurasia Journal of Mathematics, Science and Technology Education, 20(12), em2555. https://doi.org/10.29333/ejmste/15739

Basarir, L. (2022). Modelling AI in Architectural Education. Gazi University Journal of Science, 35(4), 1260-1278. https://doi.org/10.35378/gujs.967981

Granda-Piñan, A. R., Alameda-Villarrubia, A. y Mengual-Andrés, S. (2024). Teachers’ perceptions on the effect of in-service training in Innovative Learning Environments on the implementation of student-centred approaches. IJERI: International Journal of Educational Research and Innovation, (22), 1-13. https://doi.org/10.46661/ijeri.10636

Hays, J. D., Pfirman, S., Blumenthal, B., Kastens, K. y Menke, W. (2000). Earth science instruction with digital data. Computer & Geosciences, 6, 657-668. https://doi.org/10.1016/S0098-3004(99)00101-6

Hibert, C., Mangeney, A., Grandjean, G. y Baillard, C. (2014). Automated identification, location, and volume estimation of rockfalls at Piton de la Fournaise volcano. Journal of Geophysical Research: Earth Surface. JGE Earth Surface, 119, 1082-1105. https://doi.org/10.1002/2013JF002970

Kerimbayev, N., Adamova, K., Shadiev, R. y Altinay, Z. (2025). Intelligent educational technologies in individual learning: a systematic literature review. Smart Learning Environments, 12, 1. https://doi.org/10.1186/s40561-024-00360-3

Kilinc, S. (2024). Comprehensive AI assessment framework: Enhancing educational evaluation with ethical AI integration. Journal of Educational Technology and Online Learning, 7, 521-540. https://doi.org/10.31681/jetol.1492695

Lee, G. G., Mun, S., Shin, M. K. y Zhai, X. (2025). Collaborative Learning with Artificial Intelligence Speakers. Science & Education. https://doi.org/10.1007/s11191-024-00526-y

Lin, C. C., Huang, A. Y. Q., y Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments, 10, 41. https://doi.org/10.1186/s40561-023-00260-y

Mazzeo, O., Monacis, L. y Contini, P. (2025). Academic success in synchronous online learning environments. Turkish Online Journal of Distance Education, 26(1), 16-28. https://doi.org/10.17718/tojde.1444067

Orok, E., Okaramee, C., Egboro, B. y Akawa. B. (2024). Pharmacy students’ perception and knowledge of chat-based artificial intelligence tools at a Nigerian University. BMC Medical Education, 24, 1237. https://doi.org/10.1186/s12909-024-06255-8

Perea-Matins, J. E. M. (2024). Design of an electronic device in the STEAM context to relate results of physical measurements with sounds, and its analysis through science teachers’ perception. IJERI: International Journal of Educational Research and Innovation, (21), 1-21. https://doi.org/10.46661/ijeri.9391

Salas-Rueda, R. A. (2024). Análisis sobre las plataformas LMS considerando el deep learning y random forest. Revista Fuentes, 26(2), 134-146. https://doi.org/10.12795/revistafuentes.2024.24123

Salas-Rueda, R. A. y Alvarado-Zamorano, C. (2024). Teachers’ perceptions about the use of learning management systems during the covid-19 pandemic considering data science. Turkish Online Journal of Distance Education, 25(1), 260-272. https://doi.org/10.17718/tojde.1090350

Salas-Rueda, R. A., Castañeda-Martínez, R., Ramírez-Ortega, J. y Martínez-Ramírez, S. M. (2025). Use of the deep learning and decision tree techniques to analyze the incorporation of technology in the educational field. Digital Education Review, 46,26-39. https://doi.org/10.1344/der.2025.46.26-39

Sumi, S. M., Zaman, M. F. y Hirose, H. (2012). A rainfall forecasting method using machine learning models and its application to the fukuoka city case. International Journal of Applied Mathematics and Computer Science, 22, 841-854. https://doi.org/10.2478/v10006-012-0062-1

Sun, Z., Crystal-Ornelas, R., Mostafa-Mousavi, S. y Wang, J. (2022). A review of Earth Artificial Intelligence. Computers and Geosciences, 159, 105034. https://doi.org/10.1016/j.cageo.2022.105034

Tarisayi, K. y Manhibi, R. (2025). Revolutionizing Education in Zimbabwe: Stakeholder Perspectives on Strategic AI Integration. Journal of Learning and Teaching in Digital Age, 10(1), 87-93. https://doi.org/10.53850/joltida.1493508

Vasconcelos, M. A. R. y Dos-Santos, R. P. (2023). Enhancing STEM learning with ChatGPT and Bing Chat as objects to think with: A case study. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2296. https://doi.org/10.29333/ejmste/13313

Publicado

2025-09-10

Edição

Secção

Tecnología

Como Citar

Percepção dos alunos sobre o agente conversacional para inovar o processo educacional de programação Python. (2025). Revista Electrónica Gestión de las Personas y Tecnología, 18(53), 48-70. https://doi.org/10.35588/s9mpgc07