|Helen MacGillivray (Australia)||email@example.com|
|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.
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:
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.
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