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Presentation 7C2. Technology, Elucidation, Statistical Thinking and Engineering Students

Helen MacGillivray (Australia)


Presentation Abstract
The key role of the statistical sciences in modern engineering is apparent to all. Although the second of the US Engineering Criteria 2000, namely "an ability to design and conduct experiments as well as to analyze and interpret data" has been quoted as stating that statistics has a dotpoint "on its own", this undervalues the diversity and extent of statistics in engineering. From regression to experimental design/analysis to SPC to MCMC to large datasets, from reliability to queueing to risk analysis to time series to image analysis, every engineering context/area will come into contact with at least some aspects of statistical thinking and techniques. Thus engineering undergraduates need an introduction to statistical thinking and concepts and to techniques they can instantly use in relevant contexts; plus a coherent and logical development that not only optimises understanding at that learning stage but also provides a basis for ongoing learning.

In statistical education, the following quotes from Garfield (1995) are indicative of the balancing between the practical and the ideological of which all thoughtful statistical educators are aware.

- "Students learn by constructing new knowledge, using their prior knowledge"
- "Students learn by active involvement in learning activities"
- "Students learn to value what they know will be assessed."

In engineering education, the combination of the diversity of their statistical needs (both immediate and longterm), with their natural suspicion of modelling and analysing randomness and variation, and always under time and competition pressure, sometimes seems to require more balancing tricks than are possible.

The role of technology in statistics education has, as in all disciplines, received considerable attention, but more attention is needed on identifying its separate roles in statistics education. These include:

- training in the use of technology, that is, statistical software
- the use of technology to reduce computation and to optimise visualisation
- the use of technology, particularly simulation and visualisation, in teaching and learning strategies
- the development of educationally-specific software to support student learning
- the use of technology in educational delivery.

This paper considers how technology can be used in engineering statistics education to facilitate the students' conceptual structure, statistical thinking and confidence through ownership of their understanding. There is no magical big technology "stick", but rather lots of smaller, integrated ways. These build on the teachers' own statistical understanding, their understanding of the needs and pressures in engineering education, and their understanding of how technology should work for the educators, not the educators for the technology. Some of the questions considered are whether and how technology can foster the individualism, judgement and the "careful thinking" (Hogg, 1991) of good statistics, and how to prevent technology accentuating the pitfall of introductory statistics - the desire for definiteness at the expense of statistical thinking.

Garfield, J., How Students Learn Statistics, International Statistical Review, 63(1), 1995, 25-34.

Hogg, R. V., Statistical Education: Improvements are Badly Needed, The American Statistician, 45, 1991, 342-343.


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