Attributing Meanings to Representations of Data: The Case of Statistical Process Control

This article is concerned with the meanings that employees in industry attribute to representations of data and the contingencies of these meanings in context. Our primary concern is to more precisely characterize how the context of the industrial process is constitutive of the meaning of graphs of data derived from this process. We draw on data from a variety of sources, including ethnographic studies of workplaces and reflections on the design of prototype learning activities, supplemented by insights obtained from trying out these activities with a range of employees. The core of this article addresses how different groups of employees react to graphs used as part of statistical process control, focusing on the meanings they ascribe to mean, variation, target, specification, trend, and scale as depicted in the graphs. Using the notion of boundary crossing, we try to characterize a method that helps employees to communicate about graphs and come to data-informed decisions.

[1]  “Meaning” and School Mathematics , 2005 .

[3]  P. Cobb,et al.  A Constructivist Alternative to the Representational View of Mind in Mathematics Education. , 1992 .

[4]  Wolff-Michael Roth,et al.  Competent Workplace Mathematics: How Signs Become Transparent in Use , 2003, Int. J. Comput. Math. Learn..

[5]  Bert van Oers.  The fallacy of decontextualization , 1998 .

[6]  David W. Zeitler.  Introduction to Quality Control , 1994 .

[7]  Martyn Hammersley,et al.  Ethnography : Principles in Practice , 1983 .

[8]  Abbie Brown,et al.  Design experiments: Theoretical and methodological challenges in creating complex interventions in c , 1992 .

[9]  N. Davies.  Mathematical skills in the Workplace , 2003 .

[10]  A. R. Crathorne,et al.  Economic Control of Quality of Manufactured Product. , 1933 .

[11]  Susan Leigh Star,et al.  Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39 , 1989 .

[12]  W. Edwards Deming,et al.  Out of the Crisis , 1982 .

[13]  C. Hoyles,et al.  Meaning in mathematics education , 2005 .

[14]  L. Schauble,et al.  Design Experiments in Educational Research , 2003 .

[15]  C. Hoyles,et al.  Situating graphs as workplace knowledge , 2007 .

[16]  Etienne Wenger,et al.  Communities of Practice: Learning, Meaning, and Identity , 1998 .

[17]  Luk N. Van Wassenhove,et al.  Behind the Learning Curve: Linking Learning Activities to Waste Reduction , 2000 .

[18]  Celia Hoyles,et al.  The Meanings of Statistical Variation in the Context of Work , 2004 .

[19]  Layth C. Alwan,et al.  The Problem of Misplaced Control Limits , 1995 .

[20]  David Guile,et al.  Characterizing the Use of Mathematical Knowledge in Boundary-Crossing Situations at Work , 2007 .

[21]  Celia Hoyles,et al.  Touching epistemologies: meanings of average and variation in nursing practice , 1999 .

[22]  Roland Caulcutt,et al.  The Rights and Wrongs of Control Charts , 1995 .

[23]  E. Wenger.  Communities of Practice: Learning, Meaning, and Identity , 1998 .

[24]  Kunita R. Gear.  The Lean Six Sigma Pocket Toolbook , 2005 .

[25]  Thomas Pyzdek,et al.  The Six Sigma Handbook , 2000 .

[26]  Kari Augustine.  Mystery Three, Find It, and Treasure Hunt , 2006 .

[27]  Y. Engeström,et al.  Expansive Learning at Work: Toward an activity theoretical reconceptualization , 2001 .

[28]  Wolff‐Michael Roth.  What is the meaning of “meaning”? A case study from graphing , 2004 .

[29]  Andrea A. diSessa,et al.  Ontological Innovation and the Role of Theory in Design Experiments , 2004 .

[30]  Celia Hoyles,et al.  Windows on Mathematical Meanings: Learning Cultures and Computers , 1996 .

[31]  Celia Hoyles,et al.  Windows on Mathematical Meanings , 1996 .

[32]  Wolff‐Michael Roth.  Mathematical Inscriptions and the Reflexive Elaboration of Understanding: An Ethnography of Graphing and Numeracy in a Fish Hatchery , 2005 .