technology and automation in workplaces: who needs to know what, and how? professor richard noss...
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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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