Promoting evaluation capacity building in a complex adaptive system

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.