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Systematic community of Practice activities evaluation through Natural Language Processing: application to research projects

Published online by Cambridge University Press:  05 April 2019

Virginie Goepp ,
Nada Matta ,
Emmanuel Caillaud  and
Françoise Feugeas
Virginie Goepp*
Affiliation:
INSA de Strasbourg – Icube 24, Bld de la Victoire - 67084 Strasbourg Cedex, France
Nada Matta
Affiliation:
UTT – TechCICO12 Rue Marie Curie CS.42060 - 10004 Troyes Cedex, France
Emmanuel Caillaud
Affiliation:
Unistra, ICube 3, rue de l'université - 67084 Strasbourg Cedex, France
Françoise Feugeas
Affiliation:
INSA de Strasbourg – Icube 24, Bld de la Victoire - 67084 Strasbourg Cedex, France
*
Author for correspondence: Virginie Goepp, E-mail: [email protected]
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Abstract

Community of Practice (CoP) efficiency evaluation is a great deal in research. Indeed, having the possibility to know if a given CoP is successful or not is essential to better manage it over time. The existing approaches for efficiency evaluation are difficult and time-consuming to put into action on real CoPs. They require either to evaluate subjective constructs making the analysis unreliable, either to work out a knowledge interaction matrix that is difficult to set up. However, these approaches build their evaluation on the fact that a CoP is successful if knowledge is exchanged between the members. It is the case if there are some interactions between the actors involved in the CoP. Therefore, we propose to analyze these interactions through the exchanges of emails thanks to Natural Language Processing. Our approach is systematic and semi-automated. It requires the e-mails exchanged and the definition of the speech-acts that will be retrieved. We apply it on a real project-based CoP: the SEPOLBE research project that involves different expertise fields. It allows us to identify the CoP core group and to emphasize learning processes between members with different backgrounds (Microbiology, Electrochemistry and Civil engineering).

Keywords

Community of practiceNatural Language Processingperformance evaluationpragmaticsresearch project
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 2: Knowledge Engineering and Management Applied to Innovation , May 2019 , pp. 160 - 171
Copyright
Copyright © Cambridge University Press 2019 

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