Designing Problems for Improved Instruction and Learning -- Linear Algebra
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929240433229824 |
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| author | Allaire, Ryan H. Reynolds, Margaret Lee, Andrew C. |
| author_facet | Allaire, Ryan H. Reynolds, Margaret Lee, Andrew C. |
| contents | One of the grand challenges of Mathematics instruction is to provide students with problems that are both accessible and have a reasonably elegant solution. Instructors commonly resort to resources like course textbooks, online-learning platforms, or other automated problem-generating software to select problems for exams and assignments. However, reliance on such tools may result in limited control over problem parameters, potentially yielding intricate solutions that impede students' understanding. This article centers on Linear Algebra, wherein we devise algorithms for reverse engineering matrices of integers with integer outcomes through operations such as the inverse, LU decomposition, and QR decomposition. The focus is on empowering instructors to manipulate matrix properties deliberately, ensuring the creation of problems that enrich instruction and foster student confidence. The intellectual endeavor of reverse engineering such problems, grounded in both theory and matrix properties, proves mutually beneficial for both students and instructors alike. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06648 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing Problems for Improved Instruction and Learning -- Linear Algebra Allaire, Ryan H. Reynolds, Margaret Lee, Andrew C. History and Overview One of the grand challenges of Mathematics instruction is to provide students with problems that are both accessible and have a reasonably elegant solution. Instructors commonly resort to resources like course textbooks, online-learning platforms, or other automated problem-generating software to select problems for exams and assignments. However, reliance on such tools may result in limited control over problem parameters, potentially yielding intricate solutions that impede students' understanding. This article centers on Linear Algebra, wherein we devise algorithms for reverse engineering matrices of integers with integer outcomes through operations such as the inverse, LU decomposition, and QR decomposition. The focus is on empowering instructors to manipulate matrix properties deliberately, ensuring the creation of problems that enrich instruction and foster student confidence. The intellectual endeavor of reverse engineering such problems, grounded in both theory and matrix properties, proves mutually beneficial for both students and instructors alike. |
| title | Designing Problems for Improved Instruction and Learning -- Linear Algebra |
| topic | History and Overview |
| url | https://arxiv.org/abs/2402.06648 |