As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. The project will collaboratively design, test and study effective and efficient ways to develop embedded assessments (EAs) of citizen science (CS) volunteer scientific inquiry skills in order to better understand the impact of these CS experiences on volunteer scientific inquiry abilities. EAs are assessment activities that are integrated into the learning experience and allow learners to demonstrate their competencies in an unobtrusive way. The acquisition of scientific inquiry skills is an essential, even defining, characteristic of citizen science experiences that has a direct influence on data quality. Methods for assessing the direct impact of CS on volunteers' scientific inquiry skills are limited. The project will result in EA measures designed for use by diverse CS projects, strategies that CS projects can use to develop EA assessment tools, and research findings that document opportunities, supports and barriers of this innovative method across a range of CS contexts. Findings and initial resources will be shared with the broad array of stakeholders in CS through conferences, workshops, peer-reviewed publication, community websites and other relevant venues. The results of this work also have the potential to generalize to other informal science learning experiences that engage the public in science The project will address two research questions: (1) What processes are useful for developing broadly applicable EA methods or measures? and (2) What can we learn about gains in volunteers' scientific inquiry skills when citizen science organizations use EA? These will be addressed through design-based research focused on two streamlining strategies. For the reframing data validation strategy, six leaders from five established citizen science projects will conduct secondary analyses of their existing databases to uncover the skill gains of CS volunteers that are currently unexplored in their data. For the common measure strategy, ten CS projects will collaborate to create and test common EA measures of select identification-based skills. Data will be gathered through meeting notes, participant interviews and action plans, and volunteer skill gains to capture process and products of each strategy. Data will be analyzed using grounded theory, multiple process techniques, multilevel models, and repeated-measures analysis of variance. The design-based-research framework will significantly expand project impacts by jump-starting evaluation of the participating CS projects and by producing initial resources for two distinct EA strategies that have the potential to dramatically alter practice and impact citizen science efforts to ultimately enable more people to learn by contributing to the science endeavor. The project will directly equip the 15 participating citizen-science projects with authentic performance tools to assess the quality of their programing, which will expand their understanding of CS volunteer skills and help them better recruit and support their varied audiences (including rural, low-income and tribal communities).
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