The National Network for Ocean and Climate Change Interpretation [NNOCCI] is a Community of Practice [CoP] dedicated to advancing the conversation on climate change, based on the principle that wide-scale training with proven communication techniques can change the national discourse around climate change to be more productive, creative, and solutions-focused.
NNOCCI CoP is a network of individuals and organizations in formal and informal education, the social sciences, climate sciences, and public policy. By 2018, the community represented more than 184 institutions in 38 states, and over
Drawing on results from a recent national study, we draw attention to the importance of the experiential learning cycle for enhancing meaningful outcomes of interpretive and educational experiences. The experiential learning cycle involves participating in a concrete experience, reflecting on that experience, drawing out lessons learned and principles from that reflection, and putting that knowledge to work in a new situation. Recent studies reveal that attention to completing all four stages of the experiential learning cycle can enhance positive outcomes for participants in educational and
Identifying causal relationships is an important aspect of research and evaluation in visitor studies, such as making claims about the learning outcomes of a program or exhibit. Experimental and quasi-experimental approaches are powerful tools for addressing these causal questions. However, these designs are arguably underutilized in visitor studies. In this article, we offer examples of the use of experimental and quasi-experimental designs in science museums to aide investigators interested in expanding their methods toolkit and increasing their ability to make strong causal claims about
Since the late 2000s, interest in the development and use of shared measures in the informal science, technology, engineering, and mathematics (STEM) education (ISE) field has increased. The intent is to build the capacity of evaluators to measure common outcomes of ISE experiences. We begin this chapter with a definition of shared measures, a description of related technical qualities of these measures, and a discussion of benefits and concerns around the use of shared measures. We then review recent conversations and developments around shared measures, including examples of observational