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
resourceevaluationMuseum and Science Center Exhibits
The linked repository contains select resources from the SICIIT NSF project (Supporting Science and Engineering Identity Change in Immersive Interactive Technologies). The project did not reach its main objective, mainly due to disruptions caused by COVID, but we hope that the materials will be a useful resource for follow-up research.
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TEAM MEMBERS:
Stefan RankAyana AllenGlen MuschioAroutis FosterKapil Dandekar
resourceprojectProfessional Development, Conferences, and Networks
The New York Hall of Science (NYSCI) will convene a two-day participatory design conference of to identify research and education opportunities in informal settings for supporting literacy concerning Artificial Intelligence (AI), especially for diverse and underserved youth whose communities are impacted by the bias in some AI processes. AI uses computer systems that simulate human intelligence. AI systems impact nearly every aspect of daily living, performing tasks underlying navigation apps, facial recognition, e-payments, and social media. AI can perpetuate inequities and biased outcomes in the culture at large. The conference will explore how to promote engagement and conceptual learning among youth about how AI works and what skills are needed to critically use and apply AI. The conference will also explore ways to support the interests of diverse and underserved children and youth in shaping AI and joining the growing STEM workforce that will use AI in their professions.
The conference will identify key features and needs with respect to AI literacy and explore the specific roles that informal learning can play in advancing AI literacy for youth in diverse and underserved communities. Participants in the conference will include designers, learning scientists, researchers, informal and formal educators, and science center professionals. Attendees will work in separate teams and as a group to explore and critique existing AI tools and learning frameworks, discuss lessons learned from promising AI literacy programs, and identify design principles and future directions for research. Specific attention will be paid to informal mechanisms of engagement, promising networks, and research-practice partnerships that take advantage of the unique affordances of informal learning and community services to accelerate AI literacy for historically excluded youth. The insights gained from this work will result in a set of research and programmatic priorities for informal institutions to promote AI literacy in culturally responsive ways. The resulting published guide and community events will broadly disseminate priorities and design principles generated by this convening to help informal learning institutions and community learning organizations identify both assets and priorities for addressing diversity, equity, access, and inclusion issues related to AI literacy.
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?
The Council for Opportunity in Education, in collaboration with TERC, seeks to advance the understanding of social and cultural factors that increase retention of women of color in computing; and implement and evaluate a mentoring and networking intervention for undergraduate women of color based on the project's research findings. Computing is unique because it ranks as one of the STEM fields that are least populated by women of color, and because while representation of women of color is increasing in nearly every other STEM field, it is currently decreasing in computing - even as national job prospects in technology fields increase. The project staff will conduct an extensive study of programs that have successfully served women of color in the computing fields and will conduct formal interviews with 15 professional women of color who have thrived in computing to learn about their educational strategies. Based on those findings, the project staff will develop and assess a small-scale intervention that will be modeled on the practices of mentoring and networking which have been established as effective among women of color who are students of STEM disciplines. By partnering with Broadening Participation in Computing Alliances and local and national organizations dedicated to diversifying computing, project staff will identify both women of color undergraduates to participate in the intervention and professionals who can serve as mentors to the undergraduates in the intervention phase of the project. Assisting the researchers will be a distinguished Advisory Board that provides expertise in broadening the representation of women of color in STEM education. The external evaluator will provide formative and summative assessments of the project's case study data and narratives data using methods of study analysis and narrative inquiry and will lead the formative and summative evaluation of the intervention using a mixed methods approach. The intervention evaluation will focus on three variables: 1) students' attitudes toward computer science, 2) their persistence in computer science and 3) their participant attitudes toward, and experiences in, the intervention.
This project extends the PIs' previous NSF-funded work on factors that impact the success of women of color in STEM. The project will contribute an improved understanding of the complex challenges that women of color encounter in computing. It will also illuminate individual and programmatic strategies that enable them to participate more fully and in greater numbers. The ultimate broader impact of the project should be a proven, scalable model for reversing the downward trend in the rates at which women of color earn bachelor's degrees in computer science.
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
Computing fields are foundational to most STEM disciplines and the only STEM discipline to show a consistent decline in women's representation since 1990, making it an important field for STEM educators to study. The explanation for the underrepresentation of women and girls in computing is twofold: a sense that they do not fit within the stereotypes associated with computing and a lack of access to computer games and technologies beginning at an early age (Richard, 2016).
Informal coding education programs are uniquely situated to counter these hurdles because they can offer additional
Overlaying Computer Science (CS) courses on top of inequitable schooling systems will not move us toward “CS for All.” This paper prioritizes the perspectives of minoritized students enrolled in high school CS classrooms across a large, urban school district in the Western United States, to help inform how CS can truly be for all.
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TEAM MEMBERS:
Jean RyooTiera TanksleyCynthia EstradaJane Margolis
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