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.
Sense-making with data through the process of visualization—recognizing and constructing meaning with these data—has been of interest to learning researchers for many years. Results of a variety of data visualization projects in museums and science centers suggest that visitors have a rudimentary understanding of and ability to interpret the data that appear in even simple data visualizations. This project supports the need for data visualization experiences to be appealing, accommodate short and long-term exploration, and address a range of visitors’ prior knowledge. Front-end evaluation
This project tackles the urgent needs of the nation to engage people of all ages in computational thinking and help them learn basic computer science concepts with a unique and innovative approach of structured in-game computer program coding. Researchers will explore the design and development of a 3D puzzle-based game, called May's Journey, in which players solve an environmental maze by using the game's pseudo code to manipulate game objects. The game is designed to teach introductory but foundational concepts of computer programming including abstraction, modularity, reusability, and debugging by focusing players on logic and concepts while asking them to type simple instructions in a simplified programming language designed for novices. The game design in this project differs from today's block-based programming learning approaches that are often too far from actual computer code, and also differs from professional programming languages which are too complex for novices. The game and its embedded programming language learning are designed to be responsive to the progress of the learner throughout the game, transitioning from pseudo code to the embedded programming language itself. Error messages for debugging are also designed to be adaptive to players' behavior in the game. Using extensive log data collected from people playing the game, researchers can study how people learn computer programming. Such knowledge can advance understanding of the learning processes in computer programming education. Additionally, this work emphasizes the use of games as informal learning environments as they are accessible and fun, drawing attention and retention of many learners of different age groups with the potential to change attitudes towards computer programming across different populations. This project is co-funded by the STEM + Computing (STEM+C) program that supports research and development to understand the integration of computing and computational thinking in STEM learning, and the Advancing Informal STEM Learning (AISL) program that funds innovative research, approaches and resources for use in a variety of settings with its overall strategy to enhance learning in informal environments.
The project's formative and summative evaluation methods, including surveys, expert reviews of learners' computer code developed in the game, and interviews, are used to gauge learners' engagement as well as learning. In exploring learning, researchers aim to understand how players build implicit computer science knowledge through gameplay and how that gameplay relates to their performance on external transfer tasks. The project will answer the following three research questions: (1) Can observers reliably detect and label patterns of gameplay that provide evidence of learning or misconceptions regarding the four computer science constructs - abstraction, modularity, debugging and semantics - that learners exhibit playing May's Journey? (2) How does learner's implicit knowledge of these computer science constructs change over time and do those patterns vary by gender and prior programming experiences? (3) Is there a strong correlation between implicit learning measures and transfer of CS concepts: modularity, debugging, semantics, and abstraction? How do these correlations vary across elements of the game? This work will result in several outcomes: game design metaphors tested for their learning and engagement value that can be abstracted and embedded in different games. This project will also contribute patterns and an understanding of how people learn and engage in problem solving using concepts of abstraction, modularity, debugging and semantics. These outcomes will lead to advancement in knowledge in the learning sciences as well as the design of educational games that enrich STEM learning, particularly in programming and computational thinking. In addition, this project will engage female participants and underserved populations through partnering organizations including National Girls Collaborative project.
This 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.
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.
DATE:
-
TEAM MEMBERS:
Ila BermanT. Donna ChenKaren RheubanQian Cai
Increasingly, the prosperity, innovation and security of individuals and communities depend on a big data literate society. Yet conspicuously absent from the big data revolution is the field of teaching and learning. The revolution in big data must match a complementary revolution in a new kind of literacy, through a significant infusion of STEM education with the kinds of skills that the revolution in 21st century data-driven science demands. This project represents a concerted effort to determine what it means to be a big data literate citizen, information worker, researcher, or policymaker; to identify the quality of learning resources and programs to improve big data literacy; and to chart a path forward that will bridge big data practice with big data learning, education and career readiness.
Through a process of inquiry research and capacity-building, New York Hall of Science will bring together experts from member institutions of the Northeast Big Data Innovation Hub to galvanize big data communities of practice around education, identify and articulate the nature and quality of extant big data education resources and draft a set of big data literacy principles. The results of this planning process will be a planning document for a Big Data Literacy Spoke that will form an initiative to develop frameworks, strategies and scope and sequence to advance lifelong big data literacy for grades P-20 and across learning settings; and devise, implement, and evaluate programs, curricula and interventions to improve big data literacy for all. The planning document will articulate the findings of the inquiry research and evaluation to provide a practical tool to inform and cultivate other initiatives in data literacy both within the Northeast Big Data Innovation Hub and beyond.
This project, a collaboration of teams at Georgia Institute of Technology, Northwestern University, and the Museum of Design Atlanta and the Museum of Science and Industry in Chicago, will investigate how to foster engagement and broadening participation in computing by audiences in museums and other informal learning environments that can transfer to at-home and in-school engagement (and vice versa). The project seeks to address the national need to make major strides in developing computing literacy as a core 21st century STEM skill. The project will adapt and expand to new venues their current work on their EarSketch system which connects computer programming concepts to music remixing, i.e. the manipulation of musical samples, beats and effects. The initiative involves a four-year process of iteratively designing and developing a tangible programming environment based on the EarSketch learning environment. The team will develop three new applications: TuneTable, a multi-user tabletop exhibit for museums; TunePad, a smaller version for use at home and in schools; and an online connection between the earlier EarSketch program and the two new devices.
The goal is to: a) engage museum learners in collaborative, playful programming experiences that create music; b) direct museum learners to further learning and computational music experiences online with the EarSketch learning environment; c) attract EarSketch learners from local area schools to visit the museum and interact with novice TuneTable users, either as mentors in museum workshops or museum guests; and d) inform the development of a smaller scale, affordable tangible-based experience that could be used at homes or in smaller educational settings, such as classrooms and community centers. In addition to the development of new learning experiences, the project will test the hypothesis that creative, playful, and social engagement in the arts with computer programming across multiple settings (e.g. museums, homes, and classrooms) can encourage: a) deeper learner involvement in computer programming, b) social connections to other learners, c) positive attitudes towards computing, and d) the use and recognition of computational concepts for personal expression in music. The project's knowledge-building efforts include research on four major questions related to the goals and evaluation processes conducted by SageFox on the fidelity of implementation, impact, success of the exhibits, and success of bridging contexts. Methods will draw on the Active Prolonged Engagement approach (unobtrusive observation, interviews, tracking-and-timing, data summaries and team debriefs) as well as Participatory Action Research methods.
This work 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.
DATE:
-
TEAM MEMBERS:
Michael HornBrian MagerkoJason Freeman
Supported in major part by the National Science Foundation, The Human Spark (THS) project includes a three-part national PBS television series hosted by Alan Alda and a multifaceted outreach initiative to engage public television stations and their partner science museums nationwide in order to extend the utilization and impact of the project. As an independent evaluator, Multimedia Research was contracted by Thirteen to capture how the collaboration between television station and science museum outreach grantees and their respective outreach activities meet the stated goals of the outreach
In 2013 and 2014, the Museum of Science (MOS) partnered with Dr. Rob Wood’s lab at Harvard University’s School of Engineering and Applied Sciences (SEAS) to create an exhibition about Wood’s Robotic Bees (RoboBees) project. The Microrobotics Takes Flight exhibition (referred to in the original grant as the RoboBees exhibition) consists of three interactive components and an introductory section. The three interactive components are modeled on the three different engineering teams working on the RoboBees project: the Brain, the Body, and the Colony teams. The purpose of the evaluation was
To better help museum visitors make sense of large data sets, also called “Big Data”, this study focused on the types of visual representations visitors recognize, and how they make meaning (or not) of various visuals. Individual adults and youths were shown five different data visualizations (one from each of five categories), one at a time, and asked if the visualization looked familiar and how it was read. This study found that Context and previous experience matters. Participants of all ages are familiar with a wide variety of visual displays of data. If a participant encounters a visual
To better help museum visitors make sense of large data sets, also called “big data”, this study focuses on what museum visitors felt individual layers of a visual (alone and in combination with other layers) were communicating to them as the visual was constructed or deconstructed layer by layer. A second, smaller study, collected data to better understand how adult visitors would construct large data visualizations. This study was concerned with how people make sense of “big data” in their daily lives and how they engage with reference systems. The primary study used four different “big data
To better understand how audiences in public spaces, in this case those in a museum setting, relate to and make sense of the phrases “Big Data” and “Data Visualizations”, this study investigated visitors understanding of these terms. This formative study used intercepts; approaching adult visitors and inviting them to participate in a very brief interview. If the person agreed, they were asked additional questions. The first question asked about awareness of the phrase, “Big Data” or for a very small comparison group, “Data Visualization.” Visitors were then asked “How would you explain “Big
To better help museum visitors make sense of large data sets, also called “big data”, this study investigated if there were generalizable ways in which visitors engage with and then make meaning of such data sets. This front-end study was designed to explore if there were different, distinct, and repeatable patterns intuited by individuals as they work with large data sets. This was a descriptive, process method using a complex card sort with an interview. Each card had the name of one food item written on it. Food items were diverse, including eggs, crackers, lasagna, apples, tofu and almonds