AHA! Island is a new project that uses animation, live-action videos, and hands-on activities to support joint engagement of children and caregivers around computational thinking (CT) concepts and practices. Education Development Center (EDC), WGBH’s research partner for the project, conducted an impact study with 108 English-speaking families (4- to 5-year-old children and their families) to test the promise of this CT learning intervention.
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TEAM MEMBERS:
Marisa WolskyHeather LavigneJessica AndrewsAshley Lewis-PresserLeslie CuellarRegan VidiksisCamille Ferguson
Integrating science, technology, engineering, and mathematics (STEM) subjects in pre-college settings is seen as critical in providing opportunities for children to develop knowledge, skills, and interests in these subjects and the associated critical thinking skills. More recently computational thinking (CT) has been called out as an equally important topic to emphasize among pre-college students. The authors of this paper began an integrated STEM+CT project three years ago to explore integrating these subjects through a science center exhibit and a curriculum for 5-8 year old students. We
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TEAM MEMBERS:
Morgan HynesMonica CardellaTamara MooreSean BrophySenay PurzerKristina TankMuhsin MeneskeIbrahim YeterHoda Ehsan
For the past two decades, researchers and educators have been interested in integrating engineering into K-12 learning experiences. More recently, computational thinking (CT) has gained increased attention in K-12 engineering education. Computational thinking is broader than programming and coding. Some describe computational thinking as crucial to engineering problem solving and critical to engineering habits of mind like systems thinking. However, few studies have explored how computational thinking is exhibited by children, and CT competencies for children have not been consistently defined
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
AHA! Island is a new project that uses animation, live-action videos, and hands-on activities to support joint engagement of children and caregivers around computational thinking concepts and practices. This research is intended to examine the extent to which the prototyped media and activity sets support the project’s learning goals. Education Development Center (EDC), WGBH’s research partner for the project, conducted a small formative study with 16 English-speaking families (children and their caregivers) to test out these media and activity set prototypes. During the in-person video
Children’s worlds are increasingly populated by intelligent technologies. This has raised a number of questions about the ways in which technology can change children’s ideas about important concepts, like what it means to be alive or smart. In this study, we examined the impact of experience with intelligent technologies on children’s ideas about robot intelligence. A total of 60 children aged 4 through 7 were asked to identify the intellectual, psychological, and biological characteristics of 8 entities that differed in terms of their life status and intellectual capabilities. Results
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
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.
This paper argues that the "kindergarten approach to learning" – characterized by a spiraling cycle of Imagine, Create, Play, Share, Reflect, and back to Imagine – is ideally suited to the needs of the 21st century, helping learners develop the creative-thinking skills that are critical to success and satisfaction in today’s society. The paper discusses strategies for designing new technologies that encourage and support kindergarten-style learning, building on the success of traditional kindergarten materials and activities, but extending to learners of all ages, helping them continue to
This collaborative project between Tufts University and the Massachusetts Institute of Technology is researching and developing a new version of the Scratch programming language to be called ScratchJr, designed specifically for early childhood education (K-2). The current version of Scratch, which is widely implemented, is intended for ages 8-16 and is not developmentally appropriate for young children. This work will provide research-based evidence regarding young children's abilities to use an object-oriented programming language and to study the impact this has on the children's learning of scientific concepts and procedures. The team will develop ScratchJr in an iterative cycle, testing it in both in the Devtech lab at Tufts and the Eliot Pearson lab school and with a wider network of early childhood partners. At the end of the three-year project, ScratchJr will have been tested with approximately 350 students in K-2, 40 parents, and 58 early childhood educators. ScratchJr will have three components: 1) a developmentally appropriate interface, with both touch screen and keyboard/mouse options; 2) an embedded library of curricular modules with STEM content to meet federal and state mandates in early childhood education; and 3) an on-line resource and community for early childhood educators and parents. The research questions focus on whether ScratchJr can help these young children learn foundational knowledge structures such as sequencing, causality, classification, composition, symbols, patterns, estimation, and prediction; specific content knowledge; and problem solving skills. This interdisciplinary proposal makes contributions to the fields of learning technologies, early childhood education and human computer interaction. ScratchJr has the potential for broad implementation in both formal and informal settings.