JOURNAL ARTICLE

Linear algebra in engineering: a study of specialized knowledge of Chilean and Brazilian teachers.

  • Published In: Teaching Mathematics & its Applications, 2024, v. 43, n. 3. P. 179 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bianchini, Barbara Lutaif; Gomes, Eloiza; González, Marcela Parraguez; Lima, de Gabriel Loureiro 3 of 3

Abstract

This article focuses on comparing the specialized knowledge of Linear Algebra (LA) teachers in Engineering courses at higher education institutions in Chile and Brazil, using interviews analyzed through the Mathematics Teacher’s Specialized Knowledge (MTSK) model. The study identifies that teachers from both countries predominantly demonstrate strong Mathematical Knowledge (MK), especially in the Knowledge of Topics (KoT) subdomain, and Pedagogical Content Knowledge (PCK), particularly in Knowledge of Mathematics Teaching (KMT) and Knowledge of Mathematics Learning Standards (KMLS). Findings reveal limited but occasional use of digital technologies, recognition of student difficulties mainly with vector spaces and linear transformations, and that dialogues between LA and engineering discipline teachers are generally informal and personal rather than institutionalized. The research highlights the need for curricular reform and enhanced teacher training to better contextualize LA teaching within engineering, emphasizing collaboration between mathematics and engineering educators to improve student learning outcomes.

Additional Information

  • Source:Teaching Mathematics & its Applications. 2024/09, Vol. 43, Issue 3, p179
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2024
  • ISSN:0268-3679
  • DOI:10.1093/teamat/hrad007
  • Accession Number:179512907
  • Copyright Statement:Copyright of Teaching Mathematics & its Applications is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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