Skip to main content
Log in

Conceptual thinking and metrology concepts

  • Discussion Forum
  • Published:
Accreditation and Quality Assurance Aims and scope Submit manuscript

Abstract

In introducing the term ‘concept’, the authors of the 2008 International vocabulary of metrology ‘Basic and general concepts and associated terms’ (VIM, 2008) recognize that in order to operationalize a globally accepted set of metrology terms, one requires to deal with a higher level of abstraction. Concepts are obviously not specific to metrology–handling complex tasks in any domain of knowledge that requires conceptual thinking abilities. In this short white paper, we discuss how to assess and develop conceptual thinking of professionals in service, business, and industrial environments. The approach builds on a proven methodology called MERLO that has been developed in the last 15 years by experts in psychology and education with adaptation to new interactive technologies such as clickers and internet-based formative assessments. MERLO pedagogy can be used to assess individuals’ inherent conceptual thinking abilities and train them to enhance their competence in analyzing complex conceptual situations. This is pertinent to the education of metrology, quality, and statistical thinking. We suggest that MERLO can be considered as a complementary enabler to VIM, so that this fundamental work can enhance its impact and applicability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. JCGM 200:2008 International vocabulary of metrology—basic and general concepts and associated terms (VIM3). www.bipm.org

  2. De Bievre P (2008) Essential for metrology in chemistry, but not yet achieved: truly internationally understood concepts and associated terms. Metrologica 45:335–341

    Article  Google Scholar 

  3. Shafrir U (1999) Representational competence. In: Sigel IE (ed) The development of mental representation: theory and applications. Lawrence Erlbaum Publishers, Mahwah, NJ, pp 371–389

    Google Scholar 

  4. Etkind M, Kenett RS, Shafrir U (2010) The evidence based management of learning: diagnosis and development of conceptual thinking with meaning equivalence reusable learning objects (MERLO). In: 8th International conference on teaching statistics (ICOTS), Ljubljana, Slovenia

  5. Kenett RS, Kenett DA (2008) Quality by design applications in biosimilar technological products, ACQUAL. Accred Qual Assur 13:681–690

    Article  CAS  Google Scholar 

  6. Shafrir U, Eagle M (1995) Response to failure, strategic flexibility and learning. Int J Behav Develop 18:677–700

    Article  Google Scholar 

  7. Shafrir U, Ogilvie M, Bryson M (1990) Attention to errors and learning: across-task and across-domain analysis of the post-failure reflectivity measure. CognDevelop 5:405–425

    Google Scholar 

  8. Shafrir U, Pascual-Leone J (1990) Post-failure reflectivity/impulsivity and spontaneous attention to errors. J Educ Psychol 82:378–387

    Article  Google Scholar 

  9. Dybkaer R (2010) ISO terminological analysis of the VIM3 concepts ‘quantity’ and ‘kind-of-quantity’. Metrologica 47:127–134

    Article  Google Scholar 

  10. Meinrath G (2008) Lectures for chemists on statistics. I. Belief, probability, frequency, and statistics: decision making in a floating world. ACQUAL 13:3–9

    CAS  Google Scholar 

  11. Cabre MT (1998) Terminology: theory, methods, and applications. Johns Benjamins Publishing, Amsterdam

    Google Scholar 

  12. Kittredge RI (1983) Semantic processing of texts in restricted sublanguages. In: Cercone NL (ed) Computational linguistics

  13. Einstein A, Infeld L (1938) The evolution of physics: from early concepts to relativity and quanta. Simon and Shuster, NY

    Google Scholar 

  14. Leibold C, Krieger HU, Spies M (2010) Ontology based modelling and reasoning in operational risks. In: Kenett RS, Raanan Y (eds) Operational risk management: a practical approach to intelligent data analysis. ISBN 9780470517666. Wiley, Chichester

    Google Scholar 

  15. Dybkaer R (2004) An ontology on property for physical, chemical, and biological systems APMIS 112(117):210. http://www.iupac.org/publications/ci/2005/2703/bw1_dybkaer.html

Download references

Acknowledgments

The idea of writing this paper came from discussions with Prof. Paul De Bievre who identified the challenge of communicating concepts in VIM, as opposed to only terminology. We also benefited from suggestions by Dr. Alex Weisman from ChemAgis who shared with us his experience in the pharmaceutical industry. We gratefully acknowledge their contributions to this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uri Shafrir.

Additional information

Papers published in this section do not necessarily reflect the opinion of the Editors, the Editorial Board and the Publisher.

A critical and constructive debate in the Discussion Forum or a Letter to the Editor is strongly encouraged!

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shafrir, U., Kenett, R.S. Conceptual thinking and metrology concepts. Accred Qual Assur 15, 585–590 (2010). https://doi.org/10.1007/s00769-010-0669-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00769-010-0669-6

Keywords

Navigation