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Interactive Statistics: can we use experience from a large diverse student cohort to provide professional development for a wider population?

Hilliam, Rachel and Calvert, Carol (2017). Interactive Statistics: can we use experience from a large diverse student cohort to provide professional development for a wider population? New Directions in the Teaching of Physical Sciences, 12(1)

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Abstract

Statistics pervades everyone’s lives, whether it is through media coverage, reading reports, weighing up internal risks or evaluating decisions. Most managers are swamped by endless targets, dashboards and spreadsheets, but few organisations have enough employees with statistical skills to support the requirement for evidence based decision making. The need to equip today’s workforce to deal with the increased amount of data is imperative. The shortage of mathematics and statistics teachers is also likely to increase as the Government aims by 2020 to provide all post 16 students with continued mathematical education until the age of 18. At The Open University we have devised a highly successful first level statistics module which is simultaneously studied by students across a range of different disciplines, many of whom encounter statistics at some point in their qualification. This has been achieved by using topics which are of interest to everyone rather than being discipline specific. The module has produced some impressive results particularly when analysing the progress of different cohorts of students, not just from varying disciplines, but also across a broad spectrum of students with differing backgrounds. It is thought that by adapting these techniques it is possible to produce high quality statistics provision to be widely used as professional career development for employees.

Item Type: Journal Item
Copyright Holders: 2017 Unknown
ISSN: 2051-3615
Keywords: Statistics; Interactive; Career and Professional Development
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 48799
Depositing User: Rachel Hilliam
Date Deposited: 28 Feb 2017 10:57
Last Modified: 02 May 2018 14:27
URI: http://oro.open.ac.uk/id/eprint/48799
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