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
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.
The Colleges of Science & Engineering and Graduate Education, and the Metro Academies College Success Program (Metro) at San Francisco State University in partnership with San Francisco Unified School District and the San Francisco Chamber of Commerce develop an integrated approach for computing education that overcomes obstacles hampering broader participation in the U.S. science, technology, engineering and mathematics (STEM) workforce. The partnership fosters a more diverse and computing-proficient STEM workforce by establishing an inclusive education approach in computer science (CS), information technology, and computer engineering that keeps students at all levels engaged and successful in computing and graduates them STEM career-ready.
Utilizing the collective impact framework maximizes the efficacy of existing regional organizations to broaden participation of groups under-educated in computing. The collective impact model establishes a rich context for organizational engagement in inclusive teaching and learning of CS. The combination of the collective impact model of social agency and direct engagements with communities yields unique insights into the views and experiences of the target population of students and serves as a platform for national scalable networks.
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TEAM MEMBERS:
Keith BowmanIlmi YoonLarry HorvathEric HsuJames Ryan
This project will capitalize on the power of story to teach foundational computational thinking (CT) concepts through the creation of animated and live-action videos, paired with joint media engagement activities, for preschool children and their parents. Exposure at a young age to CT is critical for preparing all students to engage with the technologies that have become central to nearly every occupation. But despite this recognized need, there are few, if any, resources that (1) introduce CT to young children; (2) define the scope of what should be taught; and (3) provide evidence-based research on effective strategies for bringing CT to a preschool audience. To meet these needs, WGBH and Education Development Center/Center for Children and Technology (EDC/CCT) will utilize an iterative research and design process to create animated and live-action videos paired with joint media engagement activities for parents and preschool children, titled "Monkeying Around". Animated videos will model for children how to direct their curiosity into a focused exploration of the problem-solving process. Live-action videos will feature real kids and their parents and will further illustrate how helpful CT can be for problem solving. With their distinctive visual humor and captivating storytelling, the videos will be designed to entice parents to watch alongside their children. This is important since parents will play an important role in guiding them in explorations that support their CT learning. To further promote joint media engagement, hands-on activities will accompany the videos. Following the creation of these resources, an experimental impact study will be conducted to capture evidence as to if and how these resources encourage the development of young children's computational thinking, and to assess parents' comfort and interest in the subject. Concurrent with this design-based research process, the project will build on the infrastructure of state systems of early education and care (which have been awarded Race to the Top grants) and local public television stations to design and develop an outreach initiative to reach parents. Additional partners--National Center for Women & Information Technology, Code in Schools, and code.org (all of whom are all dedicated to promoting CT)--will further help bring this work to a national audience.
Can parent/child engagement with digital media and hands-on activities improve children's early learning of computational thinking? To answer this question, WGBH and EDC/CCT are collaborating on a design-based research process with children and their parents to create Monkeying Around successive interactions. The overarching goal of this mixed-methods research effort is to generate evidence that supports the development of recommendations around the curricular, instructional, and contextual factors that support or impede children's acquisition of CT as a result of digital media viewing and hands-on engagement. Moving through cycles of implementation, observation, analysis, and revision over the course of three years, EDC/CCT researchers will work closely with families and WGBH's development team to determine how children learn the fundamentals of CT, how certain learning tasks can demonstrate what children understand, how to stimulate interest in hands-on activities, and the necessary scaffolds to support parental involvement in the development of children's CT. Each phase of the research will provide rich feedback to inform the next cycle of content development and will include: Phase 1: the formulation of three learning blueprints (for algorithmic thinking, sequencing, and patterns); Phase 2: the development of a cohesive set of learning tasks to provide evidence of student learning, as well as the production of a prototype of the digital media and parent/child engagement resources (algorithmic thinking); Phase 3-Part A: pilot research on the prototype, revisions, production of two additional prototypes (sequencing and patterns); Phase 3-Part B: pilot research on the three prototypes and revisions; and Phase 4: production of 27 animated and live-action videos and 18 parent/child engagement activities and a study of their impact. Through this process, the project team will build broader knowledge about how to design developmentally appropriate resources promoting CT for preschool children and will generate data on how to stimulate interest in hands-on activities and the necessary scaffolds to support parental involvement in the development of children's CT. The entire project represents an enormous opportunity for WGBH and for the informal STEM media field to learn more about how media can facilitate informal CT learning in the preschool years and ways to broaden participation by building parents' capacity to support STEM learning. 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.