Abstract
In the context of education, measurement is often defined as the process of assigning a numerical value to an attribute of an object or event. Using three case studies of “process improvement”, the purpose of this article is to show how measurement takes place in manufacturing industry. We ask: What are the key issues involved in measurement that should inform the design of education and training for measurement? First, our research suggests that the definition of measurement should be enhanced so as to include the quantification of processes and multivariate constructs that aim to capture key performance indicators of production processes. Secondly, technology plays a complex role because it can lead not only to automation and increased invisibility of data, but also to the availability of information otherwise not accessible. Thirdly, we suggest that the use of the inferentialist concept of “web of reasons” in addition to more commonly used concepts such as “practice” or “activity” can help to focus not only on the what and how, but also on the why of measurement.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
This quote is from the online version: http://www.nctm.org/standards/content.aspx?id=26858.
References
Bakker, A., Wijers, M., Jonker, V., & Akkerman, S. F. (2011). The use, nature and purposes of measurement in intermediate-level occupations. ZDM The International Journal on Mathematics Education, 43(5). doi:10.1007/s11858-011-0328-3 (this issue).
Bakker, A., & Derry, J. (2011). Lessons from inferentialism for statistics education. Mathematical Thinking and Learning, 13, 5–26.
Bakker, A., Hoyles, C., Kent, P., & Noss, R. (2006). Improving work processes by making the invisible visible. Journal of Education and Work, 19, 343–361.
Bakker, A., Kent, P., Noss, R., & Hoyles, C. (2009). Alternative representations of statistical measures in computer tools to promote communication between employees in automotive manufacturing. Technology Innovations in Statistics Education, 3(2). http://www.escholarship.org/uc/item/53b9122r. Accessed 31 May 2011.
Brandom, R. (1994). Making it explicit. Cambridge, MA: Harvard University Press.
Buys, K., & De Moor, E. (2008). Domain description measurement. In M. van den Heuvel-Panhuizen & K. Buys (Eds.), Young children learn measurement and geometry (pp. 15–36). Rotterdam: Sense Publishers.
Clements, D. H. (Ed.). (2003). Learning and teaching measurement (National Council of Teachers of Mathematics Yearbook 2003). Reston, VA: National Council of Teachers of Mathematics.
George, M. L., Rowlands, D., Price, M., & Maxey, J. (2005). The lean six sigma pocket toolbook. New York: McGraw-Hill.
Gigerenzer, G., Hertwig, R., van den Broek, E., Fasolo, B., & Katsikopoulos, K. V. (2005). ‘A 30% chance of rain tomorrow’: How does the public understand probabilistic weather forecasts? Risk Analysis, 25(3), 623–629.
Gooya, Z., Khosroshani, L. G., & Teppo, A. R. (2011). Iranian students’ measurement estimation performance involving linear and area attributes of real-world objects. ZDM The International Journal on Mathematics Education, 43(5). doi:10.1007/s11858-011-0338-1 (this issue).
Guile, D., & Young, M. (2003). Transfer and transition in vocational education: Some theoretical perspectives. In T. Tuomi-Gröhn & Y. Engeström (Eds.), School and work: New perspectives on transfer and boundary-crossing (pp. 63–84). Amsterdam: Pergamon.
Hoyles, C., Noss, R., Kent, P., & Bakker, A. (2010). Improving mathematics at work: The need for techno-mathematical literacies. London: Routledge.
Hoyles, C., Wolf, A., Molyneux-Hodgson, S., & Kent, P. (2002). Mathematical skills in the workplace. London: Science, Technology and Mathematics Council. http://www.lkl.ac.uk/research/technomaths/skills2002. Accessed 31 May 2011.
Ishikawa, K. (1990). Introduction to quality control. New York: Productivity Press.
Jablonka, E. (2003). Mathematical literacy. In A. J. Bishop, M. A. Clements, C. Keitel, J. Kilpatrick, & F. Leung (Eds.), Second international handbook of mathematics education (pp. 75–102). Dordrecht: Kluwer.
Jones, K. (2010). Measurement: Everywhere and nowhere in secondary mathematics. Informal Proceedings of the BSRLM (British Society for Research into Learning Mathematics), 30(3), 67–72. http://www.bsrlm.org.uk/IPs/ip30-3/BSRLM-IP-30-3-12.pdf. Accessed 31 May 2011.
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. New York: Cambridge University Press.
Mathews, J. (1989). Tools of change: New technology and the democratisation of work. Sydney: Pluto Press.
Montgomery, D. C., & Woodall, W. H. (2008). An overview of six sigma. International Statistical Review, 76, 329–346.
NCTM (2000). Principles and standards for school mathematics. Reston, VA: National Council of Teachers of Mathematics.
Parker, M., & Leinhardt, G. (1995). Percent: A privileged proportion. Review of Educational Research, 65, 421–481.
Smith, J. P. III (2002). Everyday mathematical activity in automobile production work. In M. E. Brenner & J. N. Moschkovich (Eds.), Everyday and academic mathematics in the classroom (Journal for Research in Mathematics Education Monograph 11) (pp. 111–130). Reston, VA: National Council of Teachers of Mathematics.
Steen, L. A. (2003). Data, shapes, symbols: Achieving balance in school mathematics. In B. Madison & L. A. Steen (Eds.), Quantitative literacy: Why literacy matters for schools and colleges (pp. 53–74). Washington, DC: The Mathematical Association of America.
Williams, J., & Wake, G. (2007). Black boxes in workplace mathematics. Educational Studies in Mathematics, 64, 317–344.
Womack, J. P., Jones, D. T., & Roos, D. (1991). The machine that changed the world: The story of lean production. New York: Harper Perennial.
Van den Heuvel-Panhuizen, M., & Elia, I. (2011). Kindergartners’ performance in length measurement and the effect of picture book reading. ZDM The International Journal on Mathematics Education, 43(5). doi:10.1007/s11858-011-0331-8 (this issue).
Zuboff, S. (1988). In the age of the smart machine: The future of work and power. New York: Basic Books.
Acknowledgments
Funding of the Techno-Mathematical Literacies Research Project by the Teaching and Learning Research Programme (http://www.tlrp.org) of the United Kingdom Economic and Social Research Council is gratefully acknowledged (Award Number L139-25-0119). Funding of the Mathematical Skills in the Workplace project was by the Science Technology and Mathematics Council (now part of the SEMTA Sector Skills Council). We also acknowledge the support of NWO-PROO (Grant Number 411-06-205) in the Netherlands for Arthur Bakker’s contribution to the writing of this paper. We thank guest editor Professor Marja van den Heuvel-Panhuizen and the anonymous reviewers for their insightful comments on initial versions of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kent, P., Bakker, A., Hoyles, C. et al. Measurement in the workplace: the case of process improvement in manufacturing industry. ZDM Mathematics Education 43, 747–758 (2011). https://doi.org/10.1007/s11858-011-0359-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11858-011-0359-9