Techno-mathematical Literacies and Functional Mathematics 14-19 Celia Hoyles, Richard Noss, Phillip Kent and Arthur Bakker Institute of Education, University of London
The TLRP project on Techno-mathematical Literacies (TmL) is investigating the combinations of mathematical, statistical and technological skills that people need in workplaces. We focus on employees at “intermediate” skill level, typically non-graduates with A-level qualifications or equivalent. They may be working in a service industry such as banking as sales agents or customer enquiry agents, or in manufacturing industry as skilled operators or supervisory managers.
This research follows on from the Mathematical Skills in the Workplace project (Hoyles et al, 2002) which promoted the idea of “mathematical literacy” as a growing necessity for successful performance in the workplace. In the current project, we are using the term “Techno-mathematical Literacies” (TmL) as a way of characterising mathematics as it exists in modern, increasingly IT- based workplaces (see for example, Kent et al, 2005). We use this new term first to avoid the baggage which goes along with the disputed term “numeracy” that pervades educational discussion of mathematics; second to indicate that the mathematics involved is much more than basic arithmetic; and third to avoid the apparently simple term “mathematics” against which many people in companies may have prejudices based on their school and later life experiences. Beyond this, we are convinced that the idea of literacy is really helpful. Individuals need to be able to understand and use mathematics as a language which will increasingly pervade the workplace through IT-based systems as much as conventional literacy pervaded working life in the last century. This language exists in the form of computer inputs and outputs that have to be composed and interpreted.
Here is an illustration of what we have observed about TmL, which we will draw on to consider some issues that connect our interests in workplaces to 14-19 education and training. The illustration concerns the use of mathematical and statistical analysis in a company that provides store cards and credit cards. The company uses data about its customers’ spending habits in order to target customers for the marketing of new financial products or to encourage customers periodically to spend more using their cards. This involves drawing samples of say, 50,000 or 100,000 people from the database of several million customers in such a way as to maximise the response rates on a mail-shot to the chosen sample. This process of “data mining” is undertaken by a dedicated team of people in the company, working at the request of the marketing groups who design and manage product launch and promotional campaigns. The data miners are generally highly- qualified mathematically (technical degrees and post-graduate qualifications in statistics are typical), whereas the marketing officers generally have little exposure to formal mathematics beyond GCSE. This may seem a sensible division of labour — the maths types do the hard sums to get results for the