The University of Kansas Natural History Museum, in collaboration with the University of California Museum of Paleontology, will develop, test, and deploy an immersive educational game on the topic of evolution and common ancestry. The museum will frame the game with a narrative that involves tracing the origin of a zoonotic disease (infectious disease that is transmitted between species from animals to humans or from humans to animals). Played on the museum floor, the escape room-inspired game will explore innovative formats for museum learning and engagement. It is being designed for families with children ages 7 to 12, and by visiting groups of schoolchildren in grades 3 to 5.
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 four-year research study will investigate families' joint media engagement (JME) and informal STEM learning while listening to the child-focused STEM podcast, Brains On! Prior research has shown that the setting where families most often listen to this podcast together is the family automobile as children are being driven to school, on road trips, or other activities. Brains On! is rooted in the mission-driven principle of public radio to educate and inspire. The target audience is children 5-12 years old and their parents or caregivers. Each episode ranges from 20-45 minutes in length and presents ideas from a variety of STEM disciplines such as physics, chemistry, biology and engineering featuring sound-rich explanations of concepts through fun skits, original songs and interviews with scientists. The episodes use a light-hearted, humorous approach to share oftentimes complex STEM information. To provide an interactive experience, hosts encourage the audience to participate with the show by sending in drawings, emailing photos of plants and animals, or posing questions to be answered in future episodes. Every episode is co-hosted by a different child who interviews top scientists about their work. The scientists are selected to be representative of the range of topics presented and are meant to serve as role models for the listeners and demonstrating a wide range of career options in the STEM field.
The research adds to the social learning theory of joint media engagement (JME) which has shown that interactions between people sharing a media experience can result in learning together. Recent work on Joint Media Engagement has focused on parent/child interactions with television/video in the home. But little is known about how families engage with children's STEM podcasts together and what learning interactions occur as a result. Even less is known about this engagement within an automobile setting. This research project will build new knowledge filling a gap in the informal STEM learning field. It will use a mixed-methods research design with three phases of research to answer these questions: 1) How does the Brains On! podcast mediate STEM-based joint media engagement and family learning in an automobile setting? 2) What does STEM based joint media engagement and family learning look and sound like in this setting? 3) How do "in-automobile" factors foster or impede STEM-based joint media engagement and family learning? Phase 1 is a listener experience video study of 30 families listening to the Brains On! episodes. Phase 2 is video-based case studies of the natural automobile-based listening behaviors of eight Phase 1 families. Phase 3 is an online survey of Brains On! listeners to understand how representative the findings from Phases 1 and 2 are to the larger Brains On! Research. Results will be shared widely with key audiences that can use the findings (media developers, ISE practitioners, ISE evaluators and researchers, and families). It will also make an important contribution to the Joint Media Engagement literature and the ISE field.
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.