Lawrenz Frances, Kollmann Elizabeth Kunz, King Jean A, Bequette Marjorie, Pattison Scott, Nelson Amy Grack, Cohn Sarah, Cardiel Christopher L B, Iacovelli Stephanie, Eliou Gayra Ostgaard, Goss Juli, Causey Lauren, Sinkey Anne, Beyer Marta, Francisco Melanie
University of Minnesota, Educational Psychology, 174 EdSciB, 56 East River Rd, Minneapolis, MN, 55455, USA.
Museum of Science, Boston, Research and Evaluation Department, 1 Science Park, Boston, MA, 02114, USA.
Eval Program Plann. 2018 Aug;69:53-60. doi: 10.1016/j.evalprogplan.2018.04.005. Epub 2018 Apr 10.
This study provides results from an NSF funded, four year, case study about evaluation capacity building in a complex adaptive system, the Nanoscale Informal Science Education Network (NISE Net). The results of the Complex Adaptive Systems as a Model for Network Evaluations (CASNET) project indicate that complex adaptive system concepts help to explain evaluation capacity building in a network. The NISE Network was found to be a complex learning system that was supportive of evaluation capacity building through feedback loops that provided for information sharing and interaction. Participants in the system had different levels of and sources of evaluation knowledge. To be successful at building capacity, the system needed to have a balance between both centralized and decentralized control, coherence, redundancy, and diversity. Embeddedness of individuals within the system also provided support and moved the capacity of the system forward. Finally, success depended on attention being paid to the control of resources. Implications of these findings are discussed.
本研究呈现了一项由美国国家科学基金会资助、为期四年的案例研究结果，该研究围绕复杂自适应系统——纳米科学非正式教育网络（NISE Net）中的评估能力建设展开。“作为网络评估模型的复杂自适应系统”（CASNET）项目的结果表明，复杂自适应系统概念有助于解释网络中的评估能力建设。研究发现，NISE网络是一个复杂的学习系统，通过提供信息共享和互动的反馈回路来支持评估能力建设。系统中的参与者拥有不同水平和来源的评估知识。为了成功开展能力建设，系统需要在集中控制与分散控制、连贯性、冗余性和多样性之间取得平衡。个体在系统中的嵌入性也提供了支持，并推动了系统能力的提升。最后，成功取决于对资源控制的关注。文中讨论了这些研究结果的意义。
