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Promoting evaluation capacity building in a complex adaptive system.

Frances Lawrenz1, Elizabeth Kunz Kollmann2, Jean A King3

  • 1University of Minnesota, Educational Psychology, 174 EdSciB, 56 East River Rd, Minneapolis, MN, 55455, USA.

Evaluation and Program Planning
|April 29, 2018
PubMed
Summary
This summary is machine-generated.

Complex adaptive systems concepts explain evaluation capacity building in networks. The Nanoscale Informal Science Education Network (NISE Net) fostered evaluation capacity through feedback, interaction, and balanced control.

Keywords:
Case studiesComplex adaptive systemsEvaluation capacity buildingNetworks

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Area of Science:

  • Science Education
  • Network Science
  • Evaluation Studies

Background:

  • Evaluation capacity building is crucial for scientific networks.
  • Complex adaptive systems (CAS) offer a framework for understanding network dynamics.
  • The Nanoscale Informal Science Education Network (NISE Net) provides a case study for network evaluation.

Purpose of the Study:

  • To investigate evaluation capacity building within a complex adaptive system.
  • To determine how CAS concepts apply to network evaluation.
  • To identify factors supporting evaluation capacity in the NISE Net.

Main Methods:

  • A four-year NSF-funded case study of the NISE Net.
  • Application of Complex Adaptive Systems as a Model for Network Evaluations (CASNET) project concepts.
  • Analysis of participant knowledge, system control, and resource management.

Main Results:

  • CAS concepts effectively explain evaluation capacity building in networks.
  • NISE Net functioned as a complex learning system, promoting capacity through feedback and interaction.
  • Key success factors included balanced control, coherence, redundancy, diversity, individual embeddedness, and resource management.

Conclusions:

  • Complex adaptive systems provide a valuable lens for understanding and enhancing evaluation capacity in scientific networks.
  • Successful capacity building requires a dynamic balance of system properties and individual engagement.
  • Findings offer practical implications for managing and supporting similar networks.