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Mathematics and Engineering Education - reflections from an electrical engineer

Explore the evolving role of mathematics in engineering education through insights from an electrical engineer. From descriptive natural sciences to prescriptive engineering, discover the challenges and opportunities in the field. The increasing complexity of realized systems and technology's shift away from physics raise questions about the common math curriculum. Delve into the diverse needs and possibilities for differentiation in syllabus and learning styles, discussing the coordination with user courses and the division of labor for effective collaboration in providing relevant and motivating education.

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Mathematics and Engineering Education - reflections from an electrical engineer

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  1. Mathematics and Engineering Education- reflections from an electrical engineer Lars Lundheim Department of Eelectronic Systems NTNU

  2. Engineering = Applied Physics?

  3. Applied Chemsitry

  4. Mathematician Mathematician Engineer Engineer Physicist Physicist

  5. Physical reality Idea

  6. Models • Natural Sciences: Descripitve • Engineering: Prescriptive

  7. Steinauge

  8. Prescriptive model

  9. Silicon in nature

  10. As prescribed Si

  11. Transistor

  12. Prescribed (what we want) What we get

  13. Circuit design

  14. Circuit design

  15. Generalization Practical restrictions

  16. Digital electronics

  17. 1 000 000 000 transistors

  18. Computers as components

  19. Digital signal processing Virtually no restrictions

  20. Challenges and opportunities • The set of realizable prescriptive models has increased and is still increasing. ⇒ More math becomes relevant • The complexity of realized systems has increased and is still increasing. ⇒ More math becomes necessary. • Technology moving away from physics (nature) ⇒ Still Common math curriculum for physics and engineering?

  21. Shift of emphasis? • Basic calculus • Ordinary differential equations • Partial differential equations • Transforms • Linear algebra • Functional analysis • Numerical mathematics • Optimization theory • Statistics and probability theory

  22. Diversity of need • Differentiation of syllabus according to study program? • Differentiation of approach/learning style

  23. When to learn what • Four courses during three first semesters? • Coordination with user courses?

  24. Division of labour • How do we best collaborate to give relevant and motivating education?

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