Modeling and linear algebra in the education of mechatronics engineers
DOI:
https://doi.org/10.59741/75srm442Keywords:
Matrix transformations, robotics, automation, didactic proposal, GeoGebraAbstract
Mathematical modeling, as an approach to solving real-world problems and as an approach to teaching and learning mathematics, has gained importance in the curriculum in recent decades. In the case of Linear Algebra, systems of linear equations and linear transformations appear as models in various engineering problems and matrices allow efficient modeling of multidimensional structures. However, in multiple investigations in Mathematics Education, some issues have been identified that students face when trying to build models and apply mathematics to real-world problems. This document explores the role of modeling and Linear Algebra in solving practical problems in Mechatronics Engineering, and advances in identifying a problem related to learning these topics are presented. To this end, recurrent difficulties in the teaching and learning of modeling and Linear Algebra reported in various investigations are pointed out, with special attention to those that students face when modeling with matrix transformations and operating with matrices. The curriculum component is also analyzed in the context of the current Educational Model of the University of Sonora, specifically reviewing the Mechatronics Engineering program. The article concludes by presenting some characteristics of a didactic proposal under construction to address this problem using technology.
Downloads
References
Ardina, G. y Boholano, H. (2024). The Cognitive and Non-Cognitive Effects of GeoGebra Integration. Malaysian journal of mathematical sciences, doi: 10.47836/mjms.18.2.12 DOI: https://doi.org/10.47836/mjms.18.2.12
Batiibwe, M. (2024). Integration of GeoGebra in learning mathematics: Benefits and challenges. East African Journal of Education Studies, 7(4), 684-697. https://doi.org/10.37284/eajes.7.4.2454 DOI: https://doi.org/10.37284/eajes.7.4.2454
Blum, W., y Niss, M. (1991). Applied mathematical problem solving, modelling, applications, and links to other subjects—State, trends, and issues in mathematics instruction. Educational Studies in Mathematics, 22(1), 37–68. DOI: https://doi.org/10.1007/BF00302716
Borromeo, R. (2006). Theoretical and empirical differentiations of phases in the modelling process. ZDM, 38(2), 86–95. DOI: https://doi.org/10.1007/BF02655883
Chen, T., Liu, H., Zhou, Z., y Lin, J. (2019). Trajectory optimization of robotic manipulators using machine learning and algebraic methods. Journal of Robotics and Automation, 34(2), 245–260.
Craig, J. (2017). Introduction to robotics: Mechanics and control (4th ed.). Pearson.
Gallo, H., Verón, C., y Herrera, C. (2019). Interpretación de transformaciones lineales en el plano utilizando GeoGebra. Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología, 24, e04. https://doi.org/10.24215/18509959.24.e04 DOI: https://doi.org/10.24215/18509959.24.e04
Gómez, J. (2008). La ingeniería como escenario y los modelos matemáticos como actores. Modelling in Science Education and Learning, 1, 3–9. https://doi.org/10.4995/msel.2008.3128 DOI: https://doi.org/10.4995/msel.2008.3128
Hodge, J. (1994). The development of matrix notation and its impact on linear algebra. Historical Perspectives in Mathematics, 12(3), 150–168.
Kolman, B., y Hill, D. (2011). Elementary linear algebra with applications (9th ed.). Pearson.
Koyunkaya, M., Dede, A. (2024) Using different digital tools in designing and solving mathematical modelling problems. Educ Inf Technol 29, 19035–19065. https://doi.org/10.1007/s10639-024-12577-3 DOI: https://doi.org/10.1007/s10639-024-12577-3
Lay, D. (2015). Linear algebra and its applications (5th ed.). Pearson.
Nieto, E., y Vaca, F. (2020). Desarrollo de un modelo matemático, cinemático y dinámico con la aplicación de software, para modificar el funcionamiento de un dron, para que este realice monitoreo automático. RECIMUNDO, 4(1(Esp), 332–343. https://doi.org/10.26820/recimundo/4.(1).esp.marzo.2020.332-343 DOI: https://doi.org/10.26820/recimundo/4.(1).esp.marzo.2020.332-343
Ortiz, J., y Mendible, A. (2007). Modelización matemática en la formación de ingenieros: La importancia del contexto. https://www.researchgate.net/publication/289839859_Modelizacion_Matematica_en_la_Formacion_de_Ingenieros_La_Importancia_del_Contexto.
Panjaitan, M. (2024). Implementation Of Geogebra As A Mathematics Learning Medium By Applying A Problem-Based Learning Model (Pbm). Edumaniora : Jurnal Pendidikan dan Humaniora doi: 10.54209/edumaniora.v3i02.55. DOI: https://doi.org/10.54209/edumaniora.v3i02.55
Piñón, A., y Córdova, D. (2023). Systematic review: State of knowledge on learning difficulties and teaching strategies in linear algebra. En Proceedings of the CEUR Workshop, Vol. 3691, paper 32. CEUR-WS. https://ceur-ws.org/Vol-3691/paper32.pdf.
Spong, M., Hutchinson, S., y Vidyasagar, M. (2005). Robot modeling and control. Wiley.
Strang, G. (2016). Linear algebra and its applications (5th ed.). Wellesley-Cambridge Press.
Trigueros, M., Lozano, M., y Murillo, J. (2015). Students’ difficulties in linear algebra: The role of transformations. Educational Studies in Mathematics, 90(1), 47–65.
Universidad de Sonora (2023). Modelo Educativo de la Universidad de Sonora. Gaceta Unison.
Universidad de Sonora (2024). Programa de la asignatura Álgebra. Departamento de Ingeniería Industrial, Licenciatura en Ingeniería en Mecatrónica.
Xavier, R., Ibarra, C., y Reyes, M. E. (2019). Enseñanza-aprendizaje de las matrices en la carrera de ingeniería civil. Mikarimin. Revista Científica Multidisciplinaria, 51–60.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Universidad Autónoma Agraria Antonio Narro

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
PLUMX Metrics