NOVA Labs (pbs.org/nova/labs) is a free digital platform that engages teens and lifelong learners in activities and games that foster authentic scientific exploration. From building RNA molecules and designing renewable energy systems to tracking cloud movements and learning cybersecurity strategies, NOVA Labs participants can take part in real-world investigations by visualizing, analyzing, and playing with the same data that scientists use. Each Lab focuses on a different area of active research. But all of them illustrate key concepts with engaging and informative videos, and guide participants as they answer scientific questions or design solutions to current problems. Supporting pages on each Lab site explain the purpose and functions of the Lab, help teachers incorporate it into their classrooms, foster collaboration between users, and help users make connections to the broader world of STEM. Users are encouraged to explore potential career paths through “Meet the Scientists” profiles, and to obtain information about local and national STEM resources.
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.
The University of Pittsburgh's Center for Learning in Out-of-School Environments (UPCLOSE), the Carnegie Museum of Natural History, and the Robotics Institute at Carnegie Mellon University are building an open access cyberlearning infrastructure that employs super high-resolution gigapixel images as a tool to support public understanding, participation, and engagement with science. Networked, gigapixel image technology is an information and communication technology that creates zoomable images that viewers can explore, share, and discuss. The technology presents visual information of scientifically important content in such detail that it can be used to promote both scientific discovery and education. The purpose of the project is to make gigapixel technology accessible and usable for informal science educators and scientists by developing a robotic imaging device and online services for the creation, storage, and sharing of billion-pixel images of scientifically important content that can be analyzed visually. Project personnel are conducting design activities, user studies, and formative evaluation studies to support the development of a gigapan technology platform for demonstration and further prototyping. The project builds on and leverages existing technologies to provide informal science education organizations use of gigapixel technology for the purpose of facilitating three types of activities that promote participatory learning by the public--Public Understanding of Science activities; Public Participation in Scientific Research activities; and Public Engagement in Science activities. The long-terms goals of the work are to (1) create an accessible database of gigapixel images that informal science educators can use to facilitate public-scientist interactions and promote participatory science learning, (2) characterize and demonstrate the affordances of networked gigapixel technologies to support socially-mediated, science-focused cyberlearning experiences, (3) generate knowledge about how gigapixel technology can enable three types of learning interactions between scientists and the public around visual data, and (4) disseminate findings that describe the design, implementation, and evaluation of the gigapixel platform to support participatory science learning. The project\'s long-term strategic impacts include guiding the design of high-resolution images for promoting STEM learning in both informal and formal settings, developing an open educational resource and science communication platform, and informing informal science education professionals about the use and effectiveness of gigapixel images in promoting participatory science learning by the public.