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Assistant teaching of linear algebra based on geometric interpretation and practical application

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Published:21 January 2020Publication History

ABSTRACT

Linear algebra is not only a powerful tool in dealing with the problem of multi-variables, but also strongly logical. Students always feel that linear algebra is abstract, boring, and difficult to understand. For the teaching of linear algebra, the instructional design of linear algebra that combining the geometry intuition and practical application is proposed to help students understand the abstract knowledge. As an example in our teaching process, the geometric interpretation of matrix, similar matrices, eigenvalues and eigenvectors are given in turn. This teaching method aims to help students shift perception from visual to abstract and thus improve the teaching efficiency of linear algebra. The practical application of eigenvalue in image compression, i.e. Karhunen-Loeve transform, is presented. It is advantages to promote students' motivation in learning and cultivate their abilities in using mathematics to solve practical problems.

References

  1. STEWART S, THOMAS M O J. 2007. Embodied, symbolic and formal thinking in linear algebra. Math Educ Sci Tech. 38, 7 (Oct.2007): 927--937.Google ScholarGoogle Scholar
  2. WU Ming-yue, LI Wan-dong, WANG Bo, CAO Fu-jun.2018. Teaching exploration of geometrical meaning of matrix and linear transformation. Journal of Science of Teachers' College and University. 38, 7 (Jul.2018), 61--64.Google ScholarGoogle Scholar
  3. Wang Fei, Liao Xiaofeng, Guo Songtao, Zhan Ming. 2016. On heuristic teaching of vector space based on geometric intuition and engineering application. Journal of Southwest Normal University (Natural Science Edition).41, 3 (Mar.2016): 196--201.Google ScholarGoogle Scholar
  4. GUEUDET G. 2004. Should we teach linear algebra through geometry. Linear Algebra and its Applications. 379 (Jan.2004):491--501.Google ScholarGoogle Scholar
  5. Yan Y, Liu B, Xu X, et al.2011. The Fusion of Mathematics Experiment and Linear Algebra Practice Teaching. Information Computing and Applications. Springer Berlin Heidelberg.Google ScholarGoogle Scholar
  6. Guo Yan, Zhao Yuying. 1995.Certain application of linear algebra.Journal of Luoyang University.10, 2 (Jun.1995): 79--82.Google ScholarGoogle Scholar
  7. Caglayan, Günhan.2018. Linear algebra students' understanding of similar matrices and matrix representations of linear transformations in a MATLAB-assisted learning environment. Computers in the Schools. 35, 3 (Oct.2018): 204--225.Google ScholarGoogle Scholar
  8. Li Ji-chen, Zhao xiaoyan. 2018.The practice and design of teaching contents about cultivating students' creative thinking. College Mathematics. 34, 2 (Apr.2018):63--66.Google ScholarGoogle Scholar
  9. Zhou Hailin.2018. Analogy method for linear dependence of vectors in linear algebra. Studies in College Mathematics. 21, 1, (Apr.2018): 68--70.Google ScholarGoogle Scholar
  10. Shen Yan, Wang Feng, Fan Zhoutian. 2018.From linear operation to the definition of determinant.College mathematics.34, 5 (Oct.2018):67--71.Google ScholarGoogle Scholar
  11. Min Chao.2019. Studies on the properties of eigenvalues of matrices-thoughts of the linear algebra teaching. Education Teaching Forum.24(Jun.2019):190--191.Google ScholarGoogle Scholar
  12. Yang Wei, Gao Shuping, Chen Huaichen.2019. Integration of Matlab and linear algebra teaching in MOOC. Studies in College Mathematics. 22, 3(May.2019):60--62.Google ScholarGoogle Scholar
  13. Lesh, Richard, and Lyn D. English.2005.Trends in the evolution of models & modeling perspectives on mathematical learning and problem solving." Zentralblatt Fur Didaktik Der Mathematik. 37, 6 (Jan.2005): 487--489.Google ScholarGoogle Scholar
  14. Possani, E., et al. 2010.Use of models in the teaching of linear algebra. Linear Algebra and its Applications. 432, 8 (Jan.2010): 2125--2140.Google ScholarGoogle Scholar
  15. Ji Qiang, Shi Wenxuan, Tian Mao, Chang Shuai. 2016. Multispectral image compression based on uniting KL transform and wavelet transform. Infrared and laser engineering. 45, 2 (Feb.2016):0228004.Google ScholarGoogle Scholar

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  1. Assistant teaching of linear algebra based on geometric interpretation and practical application

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      cover image ACM Other conferences
      ICETC '19: Proceedings of the 11th International Conference on Education Technology and Computers
      October 2019
      326 pages
      ISBN:9781450372541
      DOI:10.1145/3369255

      Copyright © 2019 ACM

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      Association for Computing Machinery

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      Publication History

      • Published: 21 January 2020

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