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 proposed project broadens the utility of Public Participation in Scientific Research (PPSR) approaches, which include citizen science, to support new angles in informal learning. It also extends previous work on interactive data visualizations in museums to encompass an element of active contribution to scientific data. To achieve these goals, this project will develop and research U!Scientist (pronounced `You, Scientist!')--a novel approach to using citizen science and learning research-based technology to engage museum visitors in learning about the process of science, shaping attitudes towards science, and science identity development. Through the U!Scientist multi-touch tabletop exhibit, visitors will: (1) interact with scientific data, (2) provide interpretations of data for direct use by scientists, (3) make statements based on evidence, and (4) visualize how their data classifications contribute to globe-spanning research projects. Visitors will also get to experience the process of science, gaining efficacy and confidence through these carefully designed interactions. This project brings together Zooniverse, experts in interactive design and learning based on large data visualizations in museums, and leaders in visitor experience and learning in science museums. Over fifty thousand museum visitors are expected to interact annually with U!Scientist through this effort. This impact will be multiplied by packaging the open-source platform so that others can easily instantiate U!Scientist at their institution.
The U!Scientist exhibit development process will follow rapid iterations of design, implementation, and revision driven by evaluation of experiences with museum visitors. It will involve close collaboration between specialists in computer science, human-computer interaction and educational design, informal science learning experts, and museum practitioners. The summative evaluation will be based on shadowing observations, U!Scientist and Zooniverse.org logfiles (i.e., automated collection of user behavior metrics), and surveys. Three key questions will be addressed through this effort: Q1) Will visitors participate in PPSR activities (via the U!Scientist touch table exhibit) on the museum floor, despite all the distractions and other learning opportunities competing for their attention? If so, who engages, for how long, and in what group configurations? Q2) If visitors do participate, will they re-engage with the content after the museum visit (i.e., continue on to Zooniverse.org)? Q3) Does engaging in PPSR via the touch table exhibit--with or without continued engagement in Zooniverse.org after the museum visit--lead to learning gains, improved understanding of the nature of science, improved attitudes towards science, and/or science identity development?
The mixed methods randomized experimental study assessed a model of engagement and education that examined the contribution of SciGirls multimedia to fifth grade girls’ experience of citizen science. The treatment group (n = 49) experienced 2 hours of SciGirls videos and games at home followed by a 2.5 hour FrogWatch USA citizen science session. The control group (n = 49) experienced the citizen science session without prior exposure to SciGirls. Data from post surveys and interviews revealed that treatment girls, compared to control girls, demonstrated significantly greater interest in their
The Jackprot is a didactic slot machine simulation that illustrates how mutation rate coupled with natural selection can interact to generate highly specialized proteins. Conceptualized by Guillermo Paz-y-Miño C., Avelina Espinosa, and Chunyan Y. Bai (New England Center for the Public Understanding of Science, Roger Williams University and the University of Massachusetts, Dartmouth), the Jackprot uses simplified slot-machine probability principles to demonstrate how mutation rate coupled with natural selection suffice to explain the origin and evolution of highly specialized proteins. The
The Cornell Lab of Ornithology is creating a new type of interactive, question-driven, online bird-identification tool called "Merlin," along with associated games, social networking tools, and other media. Unlike existing bird-identification guides, which are based on traditional taxonomic keys written by scientists, Merlin uses machine learning algorithms and crowd-sourced data (information provided by thousands of people) to identify birds and improve Merlin's performance with each interaction. The tool will help millions of people identify birds and participate in a collective effort to help others. The Crowd ID project will make it easier for people to discover the names of birds, learn observation and identification skills, find more information, and appreciate Earth's biodiversity. The summative evaluation plan is measuring increases in participants' knowledge, engagement, and skills, as well as changes in behavior. Impacts on participants will be compared to a control group of users not using Merlin. Merlin tools will be integrated into the Cornell Lab's citizen science and education projects, which reach more than 200,000 participants, schoolchildren, and educators across the nation. Merlin will be broadly adapted for use on other websites, social networking platforms, exhibits, mobile devices, curricula, and electronic field guides. Once developed, Merlin can be modified to identify plants, rocks, and other animals. Merlin will promote growth of citizen science projects which depend on the ability of participants to identify a wide range of organisms.