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
This one-year Collaborative Planning project seeks to bring together an interdisciplinary planning team of informal and formal STEM educators, researchers, scientists, community, and policy experts to identify the elements, activities, and community relationships necessary to cultivate and sustain a thriving regional early childhood (ages 3-6) STEM ecosystem. Based in Southeast San Diego, planning and research will focus on understanding the needs and interests of young Latino dual language learners from low income homes, as well as identify regional assets (e.g., museums, afterschool programs, universities, schools) that could coalesce efforts to systematically increase access to developmentally appropriate informal STEM activities and resources, particularly those focused on engineering and computational thinking. This project has the potential to enhance the infrastructure of early STEM education by providing a model for the planning and development of early childhood focused coalitions around the topic of STEM learning and engagement. In addition, identifying how to bridge STEM learning experiences between home, pre-k learning environments, and formal school addresses a longstanding challenge of sustaining STEM skills as young children transition between environments. The planning process will use an iterative mixed-methods approach to develop both qualitative and quantitative and data. Specific planning strategies include the use of group facilitation techniques such as World Café, graphic recording, and live polling. Planning outcomes include: 1) a literature review on STEM ecosystems; 2) an Early Childhood STEM Community Asset Map of southeast San Diego; 3) a set of proposed design principles for identifying and creating early childhood STEM ecosystems in low income communities; and 4) a theory of action that could guide future design and research. This project is funded by the Advancing Informal STEM Learning program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments.
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.
Research on human–robot interaction has often ignored the human cognitive changes that might occur when humans and robots work together to solve problems. Facilitating human–robot collaboration will require understanding how the collaboration functions system-wide. The authors present detailed examples drawn from a study of children and an autonomous rover, and examine how children’s beliefs can guide the way they interact with and learn about the robot. The data suggest that better collaboration might require that robots be designed to maximize their relationship potential with specific users
Maker Corps increases the capacity of youth-serving organizations nationwide to engage youth and families in making. Diverse Maker Corps Members expand the current network of makers, mentors, and community leaders poised to lead creative experiences for youth. In the Maker Corps' second year evaluation report, we address the following questions: 1. How does Maker Corps impact the Maker Corps Members, participating Host Sites, and the audiences they serve? 2. In what ways can the Maker Corps program improve to better serve these participants and their audiences? We developed an evaluation plan with two primary methods: surveys and case studies. We surveyed all Maker Corps Members and Host Sites at multiple points during their service year. This method allowed us to get a broad look at Maker Ed's impact across the Maker Corps program. We balanced this approach by conducting case studies at three Host Sites, which allowed us to get a deeper, more specific look at Maker Ed's impact.
Situated in the shade of palm trees in the Miami, FL area, the REM Learning Center serves children aged 12 months to nine years. By having two dedicated Maker Corps Members during the summer, they could increase the number of visits children had to the "Play, Make, Share" studio, continue to experiment with different materials and facilitation strategies, and begin to build the expertise of their staff.