Artificial Intelligence (AI), the research and development of machines to mimic human thought and behavior, encompasses one of the most complex scientific and engineering challenges in history. AI now permeates essentially all sectors of the economy and society. Young people growing up in the era of big data, algorithms, and AI need to develop new awareness, content knowledge, and skills to understand humans’ relationships with these new technologies and become producers of AI artifacts themselves.
YR Media and MIT’s Understanding AI project researched and developed innovative approaches to
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?
Maker Education scholarship is accumulating increasingly complex understandings of the kinds of learning associated with maker practices along with principles and pedagogies that support such learning. However, even as large investments are being made to spread maker education, there is little understanding of how organizations that are intended targets of such investments learn to develop new maker related educational programs. Using the framework of Expansive Learning, focusing on organizational learning processes resulting in new and unfolding forms of activity, this paper begins to fill
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
The paper describes how middle school students appropriated and transformed a particular learning experience in an afterschool literacy program in Philadelphia. The learning experience was designed to ensure that urban African-American, middle school girls had access to technology and learned how to use it to create a web page that showcased future career aspirations. The program’s director enlisted the help of male, Caucasian high school students from the suburbs of Philadelphia to facilitate the technology learning experience for the middle school youth (both girls and boys were in the
This project is designed to improve communication between scientists and the public focusing on the role of evidence in science. It is a two-year project that includes: 1) implementing a national survey on the public use of science web sites; 2) conducting a national Science Education Outreach Forum bringing together scientists and informal science educators; 3) implementing workshop sessions at a national conference to disseminate lessons learned from the survey and Forum; and 4) developing a prototype website on the role of evidence that will be evaluated for audience engagement and understanding. This project builds on the Exploratorium's prior NSF-funded project (ESI#9980619) developing innovative strategies using the Internet to link scientists and the public using Webcasts, annotated datasets and interactive web resources. Project collaborators include the Pew Internet and American Life Project, Palmer Station, Scripps Oceanographic Institute, FermiLab and the Society of Hispanic Physicists among others. The research and evaluation of the project has the potential for strategic impact by providing new information and models on how science centers can more effectively use the Internet to improve communication between scientists and the public while engaging learners more effectively.