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Artículos

Vol. 25 No. 1 (2022): March

MENTAL STRUCTURES AND MECHANISMS THAT, FROM A GEOMETRIC PERSPECTIVE, MODEL AND ARTICULATE THE LEARNING OF VALUE AND OWN VECTOR IN R2

DOI
https://doi.org/10.12802/relime.22.2513
Submitted
November 8, 2022
Published
2022-07-25

Abstract

Two refined genetic decompositions (DG's) are proposed, as a result of the application of the APOE Research Cycle, which describe structures and mental mechanisms for the concept of value and eigenvector in two case studies. The first DG0 models the prior knowledge that high school students (14-16 years old) must achieve to build said concept in R2 at university, —this model is based on the rotation of vectors and the concept of scalar multiple—. The second DG1 models in R2 the construction of value and eigenvector in first-year university students and shows how to rely on high school topics to structure said concept from relationships between the linear transformation and the scalar multiple vector as generator of a straight line. The data analysis allows validating the DG's and outlining a cognitive path for learning the concept of value and eigenvector in R2.

References

  1. "Arnon, I., Cottril, J., Dubinsky, E., Oktaç, A., Roa-Fuentes, S., Trigueros, M. y Weller, K. (2014). APOS Theory. A framework for research and curriculum development in mathematics education. New York: Springer. Doi: 10.1007/978-1-4614-7966-6.
  2. Beltrán-Meneu, M., Murillo-Arcila, M. y Albarracin, L. (2017). Emphasizing visualization and physical applications in the study of eigevectors and eigenvalors. Teaching Mathematics and its applications 36, 123- 135. Doi: 10.1093/teamat/hrw018.
  3. Beltrán-Meneu, M., Murillo-Arcila, M. y Jordán, E. (2017). A teaching proposal for the study of eigevectors and eigenvalors. Journal of Technology and Science Education 7(1), 100 - 113. Doi: 10.3926/jotse.260.
  4. Bouhjar K., Andrews-Larson C., Haider M. y Zandieh M. (2018). Examining Students’ Procedural and Conceptual Understanding of Eigenvectors and Eigenvalues in the Context of Inquiry-Oriented Instruction. In: Stewart S., Andrews-Larson C., Berman A., Zandieh M. (eds) Challenges and Strategies in Teaching Linear Algebra. ICME-13 Monographs. Springer, Cham. Doi:10.1007/978-3-319-66811-6_9.
  5. Cook, K.L. & Bush, S.B. (2018). Design thinking in integrated STEAM learning: Surveying the landscape and exploring exemplars in elementary grades. School Science and Mathematics, 118(3-4), pp. 93-103. 2018. ISSN: 1949-8594. DOI:10.1111/ ssm.12268.
  6. Dorier, J., Robert, A., Robinet , R. y Rogalski, M. (2000) The Obstacle of Formalism in Linear Algebra. En J.-L. Dorier (Ed.). On the Teaching of Linear Algebra. Dordrecht, Netherlands: Kluwer Academic Publishers.
  7. Dorier, J.L. & Sierpinska, A. (2001). Research into the teaching and learning of linear algebra, in: D. Holton (Ed.), The Teaching and Learning of Linear Algebra at University Level, on ICMI Study, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001, pp. 255–274.
  8. Dubinsky, E. (1991). Reflective abstraction in advanced mathematical thinking, in (D. Tall, ed.) Advanced Mathematical Thinking, Dordrecht: Kluwer, 95-126.
  9. Dubinsky, E., Weller, K., McDonald, M. & Brown, A. (2005). Some historical issues and paradoxes regarding the concept of infinity: An APOS analysis: Part I. Educational Studies in Mathematics, 58(3), 335-359.
  10. González, D. y Roa-Fuentes, S. (2017). Un esquema de transformación lineal: construcción de objetos abstractos a partir de la interiorización de acciones concretas. Enseñanza de las Ciencias 35(2), 89 - 107. Doi: 10.5565/rev/ensciencias.2150.
  11. Harel, G. (2017) The learning and teaching of linear algebra: observations and generalizations. The Journal of Mathematical Behavior, 46, 69–95. Doi: 10.1016/j.jmathb.2017.02.007.
  12. Hillel, J., Sierpinska, A., & Dreyfus, T. (1998). Investigating linear transformations with Cabri. In Proceedings of the International Conference on the Teaching of Tertiary Mathematics.
  13. Klasa, J. (2010). A few pedagogical designs in linear algebra with Cabri and Maple. Linear algebra and its applications, 432(8), 2100-2111.
  14. Lay, D. (2007). Álgebra lineal y sus aplicaciones (3ª Ed.). México: Pearson educación.
  15. McDonald, M., Mathews, D. y Strobel, K. (2000). Understanding sequences: A tale of two objects. Research in Collegiate mathematics education IV. CBMS issues in mathematics education (Vol. 8, pp. 77–102). Providence, RI: American Mathematical Society.
  16. Moore, G. H. (1995). The axiomatization of linear algebra: 1875-1940. Historia Mathematica, 22, 262-303.
  17. Oktaç, A. (2018). Understanding and Visualizing Linear Transformations. In: Kaiser, G., Forgasz, H., Graven, M., Kuzniak, A., Simmt, E., Xu, B. (eds) Invited Lectures from the 13th International Congress on Mathematical Education. ICME-13 Monographs. Springer, Cham. https://doi.org/10.1007/978-3-319-72170-5_26
  18. Oktaç, A. (2019). Mental constructions in linear algebra. ZDM. Doi: 10.1007/s1185-019-01037-9.
  19. Parraguez M., Lezama, J. y Jiménez, R. (2016). Estructuras mentales para modelar el aprendizaje del teorema de cambio base de vectores. Enseñanza de las Ciencias, 34(2), 129-150. Doi: 10.5565/rev/ensciencias.1950.
  20. Parraguez, M., y Oktaç, A., (2010). Construction of the vector space concept from the view point of APOS Theory. Linear Algebra and its Applications, 432(8), 2112-2124. DOI: 10.1016/j.laa.2009.06.034.
  21. Poole, D. (2011). Álgebra Lineal. Una introducción Moderna (3º Ed.). México: Thomson.
  22. Rash, A. y Fillebrown, S. (2016). Courses on the Beauty of Mathematics: Our Version of General Education Mathematics Courses, PRIMUS, 26:9, 824-836. Doi: 10.1080/10511970.2016.1191572.
  23. Rensaa, R., Hogstad, N. y Monaghan, J. (2020). Perspectives and reflections on teaching linear algebra, Teaching Mathematics and its Applications: An International Journal of the IMA, hraa002. DOI: 10.1093/teamat/hraa002.
  24. Roa-Fuentes, S. y Oktaç, A. (2010). Construción de una descomposición genética: análisis teórico del concepto transformación lineal. Revista Latinoamericana de Investigación en Matemática Educativa, RELIME, 13(1),89-112.
  25. Robinet, J. (1986). Les réels: quels modeles en ont les éléves? Educational Studies in Mathematics, 17, 359-386.
  26. Rodríguez, M., Parraguez, M. y Trigueros, M. (2018). Construcción Cognitiva del Espacio Vectorial R^2 . RELIME: Revista Latinoamericana de Investigación en Matemática Educativa, 21(1), 57-86.
  27. Roehrig, G., Moore, T., Wang. H.H. & Park, M.S. (2012). Is Adding the E Enough? Investigating the Impact of K-12 Engineering Standards on the Implementation of STEM Integration. School Science and Mathematics, 112(1), 31-44. ISSN: 1949-8594. DOI: 10.1111/j.1949-8594.2011.00112.x.
  28. Salgado, H. y Trigueros, M. (2015). Teaching eigenvalues and eigenvectors using models and APOS theory. The Journal of Mathematical Behavior, 39, 100–120. Doi: 10.1016/j.jmathb.2015.06.005.
  29. Stake, R. E (2010). Investigación con estudio de casos. (5ª Ed.). Barcelona: Labor.
  30. Soto, J.L. (2005). Algunas dificultades en la conversión gráfico-algebraica de situaciones de vectores. En Lezama, Javier; Sánchez, Mario; Molina, Juan Gabriel (Eds.), Acta Latinoamericana de Matemática Educativa (pp. 193-199).
  31. Tall, D. (1991). The psychology of advanced mathematical thinking. En Tall, D. (Ed.), Advanced Mathematical Thinking (pp. 3-21). Kluwer Academic Publisher: Dordrecht/Boston/London.
  32. Thomas, M. O. y Stewart, S. (2011). Eigenvalues and eigenvectors: Embodied, symbolic and formal thinking. Mathematics Education Research Journal, 23(3), 275-296.
  33. Yáñez, A. (2015). Construcción de los conceptos de valores y vectores propios en R^2 y R^3 desde la teoría APOE. Tesis de Maestría no publicada, Pontificia Universidad Católica de Valparaíso. Chile."

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