This is a session of Topic 7: Statistics education and the wider society
(Thursday 15th, 14:00-16:00)
Statistics education for engineering
- Lena Zetterqvist (Sweden)
AbstractStatistical needs in engineering are indisputable and more substantial than ever. A challenge is to meet the large variety of the need. For some engineers, statistical literacy is required at the workplace. Others use statistical methods, often specific and at an advanced level, and/or sophisticated statistical and mathematical thinking in real engineering problems. In parallel, research and development is expanding in many technological sectors, creating new statistical challenges. At engineering schools, new programs — sometimes with elements of nontraditional engineering subjects — are introduced. The current focus on climate change is merely one example.
All engineering students at the undergraduate level need an introduction to statistical ideas and methods. A solid basis of basic statistical concepts and techniques is essential for further training, whether in advances courses or in the workplace. Statistic educators often experience the problem to convince engineering students of the usefulness of statistical ideas and methods in their future profession. Students actively working with real-world data in an engineering context is one way to increase motivation and to introduce them to statistical challenges within engineering. What further ways are there? Does it matter how we teach courses or which content we include? Could a differentiation of basic courses for different programs, with relevant data and context for the specific group of students, be one way to meet the challenge of the variety of statistical needs? Do we have the possibilities and/or the resources? Are statistics teachers trained to give sufficient background for the specific topics?
Statistics education for practicing engineers is inherently connected to the context of the workplace. Good in-house courses require considerations of the specific purpose of the course as well as of the background and future of the participants. How to enable participants to continue developing statistical thinking within their own contexts?