Informal STEM learning experiences (ISLEs), such as participating in science, computing, and engineering clubs and camps, have been associated with the development of youth’s science, technology, engineering, and mathematics interests and career aspirations. However, research on ISLEs predominantly focuses on institutional settings such as museums and science centers, which are often discursively inaccessible to youth who identify with minoritized demographic groups. Using latent class analysis, we identify five general profiles (i.e., classes) of childhood participation in ISLEs from data
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TEAM MEMBERS:
Remy DouHeidi CianZahra HazariPhilip SadlerGerhard Sonnert
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Today’s young people have a personal stake in their ability to function with data. Future job prospects might hinge on their ability to participate in the new data economy. But equally, young people are themselves the subjects of data. The datafication of young people’s lives leads to profound questions about childhood, technology, and the equity of access to STEM learning around data, one of which is this: How might young people be empowered in a data-centric world?
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TEAM MEMBERS:
Leanne BowlerMark RosinIrene Lopatovska
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Collaborative robots – cobots – are designed to work with humans, not replace them. What learning affordances are created in educational games when learners program robots to assist them in a game instead of being the game? What game designs work best?
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.
The FIRST Longitudinal Study is a multi-year longitudinal study assessing the impacts of FIRST’s afterschool robotics programs on the STEM related interests and educational and career trajectories of program participants. FIRST is one of the nation’s largest after-school robotics programs, serving more than 460,000 youth aged 6-18 annually through the FIRST LEGO League (Ages 7-14), the FIRST Tech Challenge (grades 7-12) and the FIRST Robotics Competition (grades 9-12). The study is tracking over 1200 program participants and comparison students, using a quasi-experimental design, over a
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TEAM MEMBERS:
Alan MelchiorCathy BurackMatthew HooverJill Marcus
This Research in Service to Practice project, a collaboration of Pepperdine University and the New York Hall of Science, will establish a network of STEM-related Media Making Clubs comprised of after-school students aged 12 - 19 and teachers in the U.S. and in three other countries: Kenya, Namibia and Finland. The media produced by the students may include a range of formats such as videos, short subject films, games, computer programs and specialized applications like interactive books. The content of the media produced by the students will focus on the illustration and teaching of STEM topics, where the shared media is intended to help other students become enthused about and learn the science. This proposal builds on the principal investigator's previous work on localized media clubs by now creating an international network in which after-school students and teachers will collaborate at a distance with other clubs. The central research questions for the project pertain to three themes at the intersection of learning, culture and collaboration: the impact of participatory teaching, virtual networks, and intercultural, global competence. The research will combine qualitative, cross-cultural and big data methods. Critical to the innovation of the project, the research team will also develop a network assessment tool, adapting epistemic network analysis methods to the needs of this initiative. 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.
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TEAM MEMBERS:
Eric HamiltonKatherine McMillanPriya Mohabir