In this collaborative project, a university research lab and children's science museum work together to design, implement, study, and revise a week-long data science camp for middle school age students, data science learning assessment items and a facilitator training curriculum.
Arecibo C3 will serve as a collaborative hub for STEM discovery and exploration by building upon existing programs and opportunities established at the Arecibo site by previous NSF programs, while also creating new STEM education, research, and outreach programs and initiatives. The goals for the Center are to (1) promote STEM education, learning, and teaching; (2) support fundamental and applied STEM and STEM education research; (3) broaden participation in STEM; and (4) build and strengthen collaborations and partnerships.
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TEAM MEMBERS:
Jose Agosto RiveraJoseph Carroll-MirandaJaime Abreu RamosAmilcar VelezJason WilliamsCristina Fernandez-MarcoWanda Diaz MercedAnuchka RamosPatricia Ordonez
resourceprojectProfessional Development, Conferences, and Networks
The New York Hall of Science (NYSCI) will convene a two-day participatory design conference of to identify research and education opportunities in informal settings for supporting literacy concerning Artificial Intelligence (AI), especially for diverse and underserved youth whose communities are impacted by the bias in some AI processes. AI uses computer systems that simulate human intelligence. AI systems impact nearly every aspect of daily living, performing tasks underlying navigation apps, facial recognition, e-payments, and social media. AI can perpetuate inequities and biased outcomes in the culture at large. The conference will explore how to promote engagement and conceptual learning among youth about how AI works and what skills are needed to critically use and apply AI. The conference will also explore ways to support the interests of diverse and underserved children and youth in shaping AI and joining the growing STEM workforce that will use AI in their professions.
The conference will identify key features and needs with respect to AI literacy and explore the specific roles that informal learning can play in advancing AI literacy for youth in diverse and underserved communities. Participants in the conference will include designers, learning scientists, researchers, informal and formal educators, and science center professionals. Attendees will work in separate teams and as a group to explore and critique existing AI tools and learning frameworks, discuss lessons learned from promising AI literacy programs, and identify design principles and future directions for research. Specific attention will be paid to informal mechanisms of engagement, promising networks, and research-practice partnerships that take advantage of the unique affordances of informal learning and community services to accelerate AI literacy for historically excluded youth. The insights gained from this work will result in a set of research and programmatic priorities for informal institutions to promote AI literacy in culturally responsive ways. The resulting published guide and community events will broadly disseminate priorities and design principles generated by this convening to help informal learning institutions and community learning organizations identify both assets and priorities for addressing diversity, equity, access, and inclusion issues related to AI literacy.
This project investigates long-term human-robot interaction outside of controlled laboratory settings to better understand how the introduction of robots and the development of socially-aware behaviors work to transform the spaces of everyday life, including how spaces are planned and managed, used, and experienced. Focusing on tour-guiding robots in two museums, the research will produce nuanced insights into the challenges and opportunities that arise as social robots are integrated into new spaces to better inform future design, planning, and decision-making. It brings together researchers from human geography, robotics, and art to think beyond disciplinary boundaries about the possible futures of human-robot co-existence, sociality, and collaboration. Broader impacts of the project will include increased accessibility and engagement at two partner museums, interdisciplinary research opportunities for both undergraduate and graduate students, a short video series about the current state of robotic technology to be offered as a free educational resource, and public art exhibitions reflecting on human-robot interactions. This project will be of interest to scholars of Science and Technology Studies, Human Robotics Interaction (HRI), and human geography as well as museum administrators, educators and the general public.
This interdisciplinary project brings together Science and Technology Studies, Human Robotics Interaction (HRI), and human geography to explore the production of social space through emerging forms of HRI. The project broadly asks: How does the deployment of social robots influence the production of social space—including the functions, meanings, practices, and experiences of particular spaces? The project is based on long-term ethnographic observation of the development and deployment of tour-guiding robots in an art museum and an earth science museum. A social roboticist will develop a socially-aware navigation system to add nuance to the robots’ socio-spatial behavior. A digital artist will produce digital representations of the interactions that take place in the museum, using the robot’s own sensor data and other forms of motion capture. A human geographer will conduct interviews with museum visitors and staff as well as ethnographic observation of the tour-guiding robots and of the roboticists as they develop the navigation system. They will produce an ethnographic analysis of the robots’ roles in the organization of the museums, everyday practices of museum staff and visitors, and the differential experiences of the museum space. The intellectual merits of the project consist of contributions at the intersections of STS, robotics, and human geography examining the value of ethnographic research for HRI, the development of socially-aware navigation systems, the value of a socio-spatial analytic for understanding emerging forms of robotics, and the role of robots within evolving digital geographies.
This project is jointly funded by the Science and Technology Studies program in SBE and Advancing Informal STEM Learning (AISL) Program in EHR.
The AI behind Virtual Humans Exhibit aims to communicate to the public about the capabilities and impact of artificial intelligence (AI) through AI technologies used in Virtual Humans including facial recognition and natural language processing. AI has and will continue to profoundly impact society in the United States and around the globe. It is important to prepare the nation’s youth and the future workforce with fundamental knowledge of AI. Informal settings, such as museums, offer open and flexible opportunities in helping youth and the general public learn about AI. Virtual Humans provide an ideal vehicle to illustrate many fields of AI, as AI is arguably the science of building intelligence that thinks and acts like humans. Led by a multidisciplinary team of researchers with expertise in AI, learning design, and assessment from the Institute for Creative Technologies at University of Southern California and the Lawrence Hall of Science at University of California, Berkeley, this project will develop a Virtual Human exhibit to engage visitors through structured conversations with a Virtual Human, while showcasing how AI drives the Virtual Human’s behavior behind the scenes. The exhibit will include collaborative learning experiences for visitors such as parent-child, siblings and peers to explore what AI is and is not, what AI is and is not capable of, and what impact it will have on their lives.
The project will investigate three research questions: (1) How can a museum exhibit be designed to engage visitor dyads in collaborative learning about AI? (2) How can complex AI concepts underlying the Virtual Human be communicated in a way that is understandable by the general public? And (3) How does and to what extent the Virtual Human exhibit increase knowledge and reduce misconceptions about AI?
The project leverages existing conversational Virtual Human technology developed through decades of collaborative research in AI, including machine vision, natural language processing, automated reasoning, character animation, and machine learning. Set in the informal setting of a museum, the exhibit will be designed following evidence-based research in Computer Supported Collaborative Learning. The project team will use a mixed methods design, drawing on design-based research methodologies and experimental studies. The research team will conduct analysis of visitor observations and interviews for iterative formative improvement. Randomized experimental studies will be conducted in both lab and naturalistic environments to gauge visitor knowledge about AI. Quasi-experimental analyses will be performed to study the relationship between engagement with exhibit features and AI knowledge. The project will produce an interactive exhibit with a Virtual Human installed at the Lawrence Hall of Science and other participating museums, and instruments to measure AI learning. The project will also produce a website where visitors can experience parts of the exhibit online and continue more in-depth learning about AI and the Virtual Human technology. The project holds the potential for producing theoretical and practical advances in helping the general public develop an understanding of AI capability and ethics, advancing knowledge in the process through which young learners develop knowledge about AI, and formulating design principles for creating collaborative learning experiences in informal settings. The results will be disseminated through conference presentations, scholarly publications, and social media. The Virtual Human exhibit will be designed for dissemination and made available for installations at informal science education communities.
Virtual Reality (VR) shows promise to broaden participation in STEM by engaging learners in authentic but otherwise inaccessible learning experiences. The immersion in authentic learner environments, along with social presence and learner agency, that is enabled by VR helps form memorable learning experiences. VR is emerging as a promising tool for children with autism. While there is wide variation in the way people with autism present, one common set of needs associated with autism that can be addressed with VR is sensory processing. This project will research and model how VR can be used to minimize barriers for learners with autism, while also incorporating complementary universal designs for learning (UDL) principles to promote broad participation in STEM learning. 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. This project will build on a prototype VR simulation, Mission to Europa Prime, that transports learners to a space station for exploration on Jupiter's moon Europa, a strong candidate for future discovery of extraterrestrial life and a location no human can currently experience in person. The prototype simulation will be expanded to create a full, immersive STEM-based experience that will enable learners who often encounter cognitive, social, and emotional barriers to STEM learning in public spaces, particularly learners with autism, to fully engage and benefit from this STEM-learning experience. The simulation will include a variety of STEM-learning puzzles, addressing science, mathematics, engineering, and computational thinking through authentic and interesting problem-solving tasks. The project team's learning designers and researchers will co-design puzzles and user interfaces with students at a post-secondary institute for learners with autism and other learning differences. The full VR STEM-learning simulation will be broadly disseminated to museums and other informal education programs, and distributed to other communities.
Project research is designed to advance knowledge about VR-based informal STEM learning and the affordances of VR to support learners with autism. To broaden STEM participation for all, the project brings together research at the intersection of STEM learning, cognitive and educational neuroscience, and the human-technology frontier. The simulation will be designed to provide agency for learners to adjust a STEM-learning VR experience for their unique sensory processing, attention, and social anxiety needs. The project will use a participatory design process will ensure the VR experience is designed to reduce barriers that currently exclude learners with autism and related conditions from many informal learning opportunities, broadening participation in informal STEM learning. Design research, usability, and efficacy studies will be conducted with teens and adults at the Pacific Science Center and Boston Museum of Science, which serve audiences with autism, along with the general public. Project research is grounded in prior NSF-funded research and leverages the team's expertise in STEM learning simulations, VR development, cognitive psychology, universal design, and informal science education, as well as the vital expertise of the end-user target audience, learners with autism. In addition to being shared at conferences, the research findings will be submitted for publication to peer-reviewed journals for researchers and to appropriate publications for VR developers and disseminators, museum programs, neurodiverse communities and other potentially interested parties.
This Innovations in Development award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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TEAM MEMBERS:
Teon EdwardsJodi Asbell-ClarkeJamie LarsenIbrahim Dahlstrom-Hakki
Increasingly, scientists and their institutions are engaging with lay audiences via media. The emergence of social media has allowed scientists to engage with publics in novel ways. Social networking sites have fundamentally changed the modern media environment and, subsequently, media consumption habits. When asked where they primarily go to learn more about scientific issues, more than half of Americans point to the Internet. These online spaces offer many opportunities for scientists to play active roles in communicating and engaging directly with various publics. Additionally, the proposed research activities were inspired by a recent report by the National Academies of Sciences, Engineering, and Medicine that included a challenge to science communication researchers to determine better approaches for communicating science through social media platforms. Humor has been recommended as a method that scientists could use in communicating with publics; however, there is little empirical evidence that its use is effective. The researchers will explore the effectiveness of using humor for communicating about artificial intelligence, climate science and microbiomes.
The research questions are: How do lay audiences respond to messages about scientific issues on social media that use humor? What are scientists' views toward using humor in constructing social media messages? Can collaborations between science communication scholars and practitioners facilitate more effective practices? The research is grounded in the theory of planned behavior and framing as a theory of media effects. A public survey will collect and analyze data on Twitter messages with and without humor, the number of likes and re-tweets of each message, and their scientific content. Survey participants will be randomly assigned to one of twenty-four experimental conditions. The survey sample, matching recent U.S. Census Bureau data, will be obtained from opt-in panels provided by Qualtrics, an online market research company. The second component of the research will quantify the attitudes of scientists toward using humor to communicate with publics on social media. Data will be collected from a random sample of scientists and graduate students at R1 universities nationwide. Data will be analyzed using descriptive statistics and regression modeling.
The broader impacts of this project are twofold: findings from the research will be shared with science communication scholars and trainers advancing knowledge and practice; and an infographic (visual representation of findings) will be distributed to practitioners who participate in research-practice partnerships. It will provide a set of easily-referenced, evidence-based guidelines about the types of humor to which audiences respond positively on social media.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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TEAM MEMBERS:
Sara YeoLeona Yi-Fan SuMichael Cacciatore
Research that seeks to understand classroom interactions often relies on video recordings of classrooms so that researchers can document and analyze what teachers and students are doing in the learning environment. When studies are large scale, this analysis is challenging in part because it is time-consuming to review and code large quantities of video. For example, hundreds of hours of videotaped interaction between students working in an after-school program for advancing computational thinking and engineering learning for Latino/a students. This project is exploring the use of computer-assisted methods for video analysis to support manual coding by researchers. The project is adapting procedures used for computer-aided diagnosis systems for medical systems. The computer-assisted process creates summaries that can then be used by researchers to identify critical events and to describe patterns of activities in the classroom such as students talking to each other or writing during a small group project. Creating the summaries requires analyzing video for facial recognition, motion, color and object identification. The project will investigate what parts of student participation and teaching can be analyzed using computer-assisted video analysis. This project is supported by NSF's EHR Core Research (ECR) program, the STEM+C program and the AISL program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. The project is funded by the STEM+Computing program, which seeks to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within disciplinary STEM teaching and learning in early childhood education through high school (preK-12). As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.
The video analysis systems will provide video summarizations for specific activities which will allow researchers to use these results to quantify student participation and document teaching practices that support student learning. This will support the analysis of large volumes of video data that are often time-consuming to analyze. The video analysis system will identify objects in the scene and then use measures of distances between objects and other tracking methods to code different activities (e.g., typing, talking, interaction between the student and a facilitator). The two groups of research questions are as follows. (1) How can human review of digital videos benefit from computer-assisted video analysis methods? Which aspects of video summarization (e.g., detected activities) can help reduce the time it takes to review the videos? Beyond audio analytics, what types of future research in video summarization can help reduce the time that it takes to review videos? (2) How can we quantify student participation using computer-assisted video analysis methods? What aspects of student participation can be accurately measures by computer-assisted video analysis methods? The video to be used for this study is drawn from a project focused on engineering and computational thinking learning for Latino/a students in an after-school setting. Hundreds of hours of video are available to be reviewed and analyzed to design and refine the system. The resulting coding will also help document patterns of engagement in the learning environment.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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TEAM MEMBERS:
Marios PattichisSylvia Celedon-PattichisCarlos LopezLeiva
Robots and robotics excite and challenge youths and adults. Unfortunately, the cost of purchasing robots or building useful robots is prohibitive for many low resource individuals and groups. This project will relieve this expense and provide an opportunity for resource limited individuals to experience the thrilling aspects of robotics by building a computer game that simulates robotic action. This project uses co-robotics wherein the participating player programs an avatar to assist in a symbiotic manner to achieve the goals of the game and participant. The game will provide access to the ideas and concepts such as programing, computational thinking and role assumption. The overarching goals are (1) to engage low-resource learners in STEM education through robotics in out-of-school spaces, and (2) to update the field of robotics-base STEM education to integrate the co-robotics paradigm.
This project is designed to gain knowledge on how co-robotics can be used in the informal education sector to facilitate the integration of computational science with STEM topics and to expand the educational use of co-robotics. Because the concept of co-robotics is new, a designed-based research approach will be used to build theoretical knowledge and knowledge of effective interventions for helping participants learn programing and computational thinking. Data will be collected from several sources including surveys, self-reports, in game surveys, pre and post-tests. These data collection efforts will address the following areas: Technology reliability, Resolution of cognitive tension around co-play, Accelerate discovery and initial engagement, Foster role-taking and interdependence with co-robots, Investigate social learning, and Validate measures using item response theory analysis. The DBR study questions are:
1.What design principles support the development of P3Gs that can effectively attract initial engagement in a free-choice OST space that offers large numbers of competing options? 2.What design principles support a P3G gameplay loop that enables learning of complex skills, computational thinking and co-robotics norms, and building of individual and career interest over the course of repeated engagement?
3.What design principles support P3Gs in attaining a high rate of re-engagement within low-resource OST settings? 4.What kinds of positive impact can P3Gs have on their proximal and distal environment? In addition, the project will research these questions about design: 1.What technical and game design features are needed to accommodate technological interruption? 2.What design elements or principles mitigate competition for cognitive resources between real-time play and understanding the co-robotic's behavior in relation to the code the player wrote for it? 3.What design elements are effective at getting learners in OST settings to notice and start playing the game? 4.What designs are effective at encouraging learners to engage with challenging content, particularly the transition from manual play to co-play? 5.What design elements help players develop a stake in the role the game offers? 6.What social behaviors emerge organically around a P3G prototype that is not designed to evoke specific social interactions?
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
The Computational Thinking in Ecosystems (CT-E) project is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. The project is a collaboration between the New York Hall of Science (NYSCI), Columbia University's Center for International Earth Science Information Network, and Design I/O. It will address the need for improved data, modeling and computational literacy in young people through development and testing of a portable, computer-based simulation of interactions that occur within ecosystems and between coupled natural and human systems; computational thinking skills are required to advance farther in the simulation. On a tablet computer at NYSCI, each participant will receive a set of virtual "cards" that require them to enter a computer command, routine or algorithm to control the behavior of animals within a simulated ecosystem. As participants explore the animals' simulated habitat, they will learn increasingly more complex strategies needed for the animal's survival, will use similar computational ideas and skills that ecologists use to model complex, dynamic ecological systems, and will respond to the effects of the ecosystem changes that they and other participants elicit through interaction with the simulated environment. Research on this approach to understanding interactions among species within biological systems through integration of computing has potential to advance knowledge. Researchers will study how simulations that are similar to popular collectable card game formats can improve computational thinking and better prepare STEM learners to take an interest in, and advance knowledge in, the field of environmental science as their academic and career aspirations evolve. The project will also design and develop a practical approach to programing complex models, and develop skills in communities of young people to exercise agency in learning about modeling and acting within complex systems; deepening learning in young people about how to work toward sustainable solutions, solve complex engineering problems and be better prepared to address the challenges of a complex, global society.
Computational Thinking in the Ecosystems (CT-E) will use a design-based study to prototype and test this novel, tablet-based collectable card game-like intervention to develop innovative practices in middle school science. Through this approach, some of the most significant challenges to teaching practice in the Next Generation Science Standards will be addressed, through infusing computational thinking into life science learning. CT-E will develop a tablet-based simulation representing six dynamic, interconnected ecosystems in which students control the behaviors of creatures to intervene in habitats to accomplish goals and respond to changes in the health of their habitat and the ecosystems of which they are a part. Behaviors of creatures in the simulation are controlled through the virtual collectable "cards", with each representing a computational process (such as sequences, loops, variables, conditionals and events). Gameplay involves individual players choosing a creature and habitat, formulating strategies and programming that creature with tactics in that habitat (such as finding food, digging in the ground, diverting water, or removing or planting vegetation) to navigate that habitat and survive. Habitats chosen by the participant are part of particular kinds of biomes (such as desert, rain forest, marshlands and plains) that have their own characteristic flora, fauna, and climate. Because the environments represent complex dynamic interconnected environmental models, participants are challenged to explore how these models work, and test hypotheses about how the environment will respond to their creature's interventions; but also to the creatures of other players, since multiple participants can collaborate or compete similar to commercially available collectable card games (e.g., Magic and Yu-Go-Oh!). NYSCI will conduct participatory design based research to determine impacts on structured and unstructured learning settings and whether it overcomes barriers to learning complex environmental science.
Cities and communities in the U.S. and around the world are entering a new era of transformational change, in which their inhabitants and the surrounding built and natural environments are increasingly connected by smart technologies, leading to new opportunities for innovation, improved services, and enhanced quality of life. The Smart and Connected Communities (SCC) program supports strongly interdisciplinary, integrative research and research capacity-building activities that will improve understanding of smart and connected communities and lead to discoveries that enable sustainable change to enhance community functioning. This project is a Research Coordination Network (RCN) that focuses on achieving SCC for medium/small size, remote, and rural communities through a polycentric (multiple centers) integrated policy, design, and technology approach. The communities served by the RCN have higher barriers to information, resources, and services than larger urban communities. To reduce this gap, the PIs propose to develop need-based R&D pipelines to select solutions with the highest potential impacts to the communities. Instead of trying to connect under-connected communities to nearby large cities, this proposal aims to develop economic opportunities within the communities themselves. This topic aligns well with the vision of the SCC program, and the proposed RCN consists of a diverse group of researchers, communities, industry, government, and non-profit partners.
This award will support the development of an RCN within the Commonwealth of Virginia which will coordinate multiple partners in developing innovations utilizing smart and connected technologies. The goal of the research coordination network is to enable researchers and citizens to collaborate on research supporting enhanced quality of life for medium, small, and rural communities which frequently lack the communication and other infrastructure available in cities. The research coordination network will be led by the University of Virginia. There are 14 partner organizations including six research center partners in transportation, environment, architecture and urban planning, and engineering and technology; two State and Industry partners (Virginia Municipal League and Virginia Center for Innovative Technology); four community partners representing health services (UVA Center for Telemedicine), small and remote communities (Weldon Cooper Center), neighborhood communities (Charlottesville Neighborhood Development), and urban communities (Thriving Cities); and two national partners which support high speed networking (US-Ignite) and city-university hubs (MetroLab). Examples of research coordination include telemedicine services, transportation services, and user-centric and community-centric utilization and deployment of sensor technologies.
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TEAM MEMBERS:
Ila BermanT. Donna ChenKaren RheubanQian Cai
Women continue to be underrepresented in computer science professions. In 2015, while 57% of professional occupations in the U.S. were held by women, only 25% of computing occupations were held by women. Furthermore, the share of computer science degrees going to women is smaller than any STEM field, even though technology careers are the most promising in terms of salaries and future growth. Research suggests that issues contributing to this lack of computer science participation begin early and involve complex social and environmental factors, including girls' perception that they do not belong in computer science classes or careers. Computer science instruction often alienates girls with irrelevant curriculum; non-collaborative pedagogies; a lack of opportunities to take risks or make mistakes; and a heavy reliance on lecture instead of hands-on, project-based learning. Computer science experiences that employ research-based gender equitable best practices, particularly role modeling, can help diminish the gender gap in participation. In response to this challenge, Twin Cities PBS (TPT), the National Girls Collaborative (NGC) and Code.org will lead Code: SciGirls! Media for Engaging Girls in Computing Pathways, a three-year project designed to engage 8-13 year-old girls in coding through transmedia programming which inspires and prepares them for future computer science studies and career paths. The project includes five new PBS SciGirls episodes featuring girls and female coding professionals using coding to solve real problems; a new interactive PBSKids.org game that allows children to develop coding skills; nationwide outreach programming, including professional development for informal educators and female coding professionals to facilitate activities for girls and families in diverse STEM learning environments; a research study that will advance understanding of how the transmedia components build girls' motivation to pursue additional coding experiences; and a third-party summative evaluation.
Code: SciGirls! will foster greater awareness of and engagement in computer science studies and career paths for girls. The PBS SciGirls episodes will feature girls and female computer science professionals using coding to solve real-world challenges. The project's transmedia component will leverage the television content into the online space in which much of 21st century learning takes place. The new interactive PBSKids.org game will use a narrative framework to help children develop coding skills. Drawing on narrative transportation theory and character identification theory, TPT will commission two exploratory knowledge-building studies to investigate: To what extent and how do the narrative formats of the Code: SciGirls! online media affect girls' interest, beliefs, and behavioral intent towards coding and code-related careers? The studies aim to advance understanding of how media builds girls' motivation to pursue computer science experiences, a skill set critical to building tomorrow's workforce. The project team will also raise educators' awareness about the importance of gender equitable computer science instruction, and empower them with best practices to welcome, prepare and retain girls in coding. The Code: SciGirls! Activity Guide will provide educators with a relevant resource for engaging aspiring computer scientists. The new media and guide will also reside on PBSLearningMedia.org, reaching 1.2 million teachers, and will be shared with thousands of educators across the SciGirls CONNECT and National Girls Collaborative networks. The new episodes are anticipated to reach 92% of U.S. TV households via PBS, and the game at PBSKids.org will introduce millions of children to coding. The summative evaluation will examine the reach and impact of the episodes, game and new activities. PIs will share research findings and project resources at national conferences and will submit to relevant publications. This project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.