Technology and automation in workplaces: who needs to

know what, and how?

Professor Richard Noss

Institute of Education, University of London

2

along a dimension of required mathematics

Studies of mathematics in workplaces

• 1996-98 Hoyles, Noss

investment bank employees

• 1997-99 Hoyles, Noss, Pozzi

nurses

• 1997-99 Hoyles, Noss, Pozzi

pilots

• 2001-2 Kent, Noss

engineers

• 2001-2 Hoyles, Wolf, Molyneux-Hodgson, Kent

food processing, tourism, , health care ...

2 starting points from preceding studies

mathematics is quite different from school

mathematics and is largely invisible

• pragmatic mental strategies • little push for generality or

appreciation of models

high levels of error in p+p tests & high level of competence at work

• tools and artefacts shape activities in ways that only become visible at times of breakdowns to routine

Aims

• characterise the mathematical needs of employees in ICT-rich workplaces

• develop appropriate mathematical understandings through iterative (co-)design of learning opportunities

Techno-mathematical Literacies at Work2003-7

Funded by the Economic and Social Research Council, UK

2003-7

Techno-mathematical Literacies(TmL)

TmL are new skills needed to be functional in IT-rich workplaces that are striving for improvements in efficiency and customer communication

why literacies?why techno-mathematical?

Phase 2. Co-design with employer-partners using

a cyclic approach of design, testing, analysis,

revision

Phase 1. Workplace ethnography:

identification & characterisation of TmL

Project methodology: two phases

Each phase opened windows on how different communities made sense of critical elements of computer inputs & outputs & symbolic artefacts

Boundary objects & boundary crossing

Boundary objects are artefacts that • stand at interface between communities of

practice• satisfy the informational requirements of each• where meanings are sources of debate so

boundary crossing may not occur

Symbolic artefacts as (potential) boundary objects

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1: Financial Service Sector

highly competitive market increasingly customer focused increasing complexity of products heavily dependent on computer systems invisibility of the model

CASE STUDY

TmL research in financial services

2 large pension/investment companies

1 specialist mortgage provider: “current account mortgage” (CAM)

Boundaries between different communities in finance industry

Researchers

customers

sales

IFAs

call centre staffActuaries

An example (1) of boundary object: pension statement

TmL in financial services

understanding key variables (e.g. interest rates, admin fees)

modelling these as relationships interpreting graphs (estimates and predictions)

Pseudo-mathematical labels

Numbers as labels (“number 27 bus”):

Credit card: “1.8% per month”

Mortgage: “5.9% per annum APR”

Graphs as qualitative “diagrams” rather than measured images of relationships

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boundary objecttechnologically-enhanced

TEBOs in pensions

modelling pension statement with spreadsheet• management charges• market value reduction

compound interest tool

explore “present value” of money with spreadsheet and interactive tool

2. Car Manufacturing

observations of

1. practice & training in 2 large car factories

2. “green belt” SPC training

CASE STUDY

Boundaries between different communities in car factories

researchers

operators

managers

teamleaders

SPC department

symbolic artefact on shopfloor

running out of timecontrol vs specification

X-bar: mean

R-bar: mean range

Control limits

Cp = 1.96

Cpk = 1.50

Hartley’s constants for SD estimation

Information in corner

process capability indices

one-number measures of how well the process is performing: your Cpk = 1.4

calculated from data, not from management

employees can be ‘beaten up’ for low Cpks

most difficult part of training

Process capability measures Definitions of Cp and Cpk

TML needed understanding & reducing variation including

• knowing the difference between common and special cause variation, and how to respond

• noticing trends & patterns in processes

graphing & interpreting time series data (control charts) including distinguishing • mean versus target• specification versus control limits

control charts & one measure values Cp & Cpk were pseudo-mathematical

‘Irrelevant’ half of the Cpk equation is greyed-

out.Ratios now

represented by moving coloured bars.

boundary objecttechnologically-enhanced

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designing to understand

Findings

TmL are new skills in ICT-rich contexts

• current theories of workplace learning and training that recapitulate school mathematics are inadequate

IT systems are based on models involving mathematics that is largely invisible

• TmL are rarely recognised by managers, or picked up ‘on the job’

Symbolic informationis is often understood by employees as pseudo-mathematics

• Information often fails to fulfil its intended role as facilitating communication across community boundaries

TmL requires engagement in authentic activities that embed work process models made more visible and manipulable through interactive software tools

• exploit the complementary expertise of employees, employers and educators

Kent, P. (2009). "In the Workplace: Learning as articulation work, and doing articulation work to understand learning". In: Vavoula, G., Pachler, N., & Kukulska-Hulme, A. M. (Eds.) Researching Mobile Learning: Frameworks, Methods and Research Designs. New York: Peter Lang. 61-76.

Bakker, A., Kent, P., Derry, J., Noss, R. & Hoyles, C. (2008). Statistical inference at work: Statistical process control as an example. Statistics Education Research Journal, 7, 2, 130-145.

Hoyles, C., Bakker, A., Kent, P., & Noss, R. (2007). “Attributing meanings to representations of data: The case of statistical process control”. Mathematical Thinking and Learning, 9, 4, 331-360.

Hoyles, C., and Noss, R. (2007). "The meanings of statistical variation in the context of work". in Lesh, R., Hamilton, E. & Kaput, J. J. (Eds.), Foundations for the Future in Mathematics Education (pages 7-35). Mahwah, NJ: Lawrence Erlbaum Associates.

Kent, P., Noss, R., Guile, D., Hoyles, C., & Bakker, A (2007). “Characterizing the use of mathematical knowledge in boundary-crossing situations at work”. Mind, Culture, and Activity 14, 1-2, 64-82.

Noss, R., Bakker, A., Hoyles, C., & Kent, P. (2007). “Situating graphs as workplace knowledge”. Educational Studies in Mathematics, 65, 3, 367 - 384.

Bakker, A., Hoyles, C., Kent, P., & Noss, R. (2006). "Improving work processes by making the invisible visible". Journal of Education and Work, 19, 4, 343-361.

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