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 paper attempts to reframe popular notions of “failure” as recently celebrated in the Maker Movement, Silicon Valley, and beyond. Building on Vossoughi et al.’s 2013 FabLearn publication describing how a focus on iterations/drafts can serve as an equity-oriented pedagogical move in afterschool tinkering contexts, we explore what it means for afterschool youth and educators to persist through unexpected challenges when using an iterative design process in their tinkering projects. More specifically, this paper describes: 1) how young women in a program geared toward increasing equitable
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.
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?
Refugee youth are particularly vulnerable to STEM disenfranchisement due to factors including limited or interrupted schooling following displacement; restricted exposure to STEM education; and linguistic, cultural, ethnic, socioeconomic, and racial minority status. Refugee youth may experience a gap in STEM skills and knowledge, and a conflict between the identities necessary for participation in their families and communities, and those expected for success in STEM settings. To conduct research to better understand these challenges, an interrelated set of activities will be developed. First, youth will learn principles of physics and computing by participating in cosmic ray research with physicists using an instructional approach that builds from their home languages and cultures. Then youth periodically share what they are learning in the cosmic ray research with their parents, siblings, and science teachers at family and community science events. Finally, youth conduct reflective research on their own STEM identity development over the course of the project. Research on learning will be conducted within and across these three strands to better understand how refugee youth develop STEM-positive identities. This project will benefit society by improving equity and diversity in STEM through (1) creating opportunities for refugee youth to participate in physics research and to develop computing skills and (2) producing knowledge on STEM identity development that may be applied more broadly to improve STEM education. Deliverables from this project include: (a) research publications on STEM identity and learning; (b) curriculum resources for teaching physics and computing to multilingual youth; (c) an online digital storytelling exhibit offering narratives about belonging in STEM research which can be shared with STEM stakeholders (policy makers, scientists, educators, etc.); and (d) an online database of cosmic ray data which will be available to physicists worldwide for research purposes. This Innovations in Development proposal 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. 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.
This program is designed to provide multiple contexts, relationships, and modes across and within which the identity work of individual students can be studied to look for convergence or divergence. To achieve this goal, the research applies a linguistic anthropological framework embedding discourse analysis in a larger ethnography. Data collected in this study include field notes, audio and video recordings of naturalistic interactions in the cosmic ray research and other program activities, multimodal artifacts (e.g., students' digital stories), student work products, interviews, and surveys. Critically, this methodology combines the analysis of identity formation as it unfolds in moment-to-moment conversations (during STEM learning, and in conversations about STEM and STEM learning) with reflective tasks and the production of personal narratives (e.g., in digital stories and interviews). Documenting convergence and divergence of STEM identities across these sources of data offers both methodological and theoretical contributions to the field. The research will offer thick description of the discursive practices of refugee youth to reveal how they construct identities related to STEM and STEM disciplines across settings (e.g., during cosmic ray research, while creating digital stories), relationships (e.g., peer, parent, teacher), and the languages they speak (e.g., English, Swahili). The findings will be of potential value to instructional designers of informal learning experiences including those working with afterschool, museums, science centers and the like, educators, and scholars of learning and identity.
This Innovations in Development award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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TEAM MEMBERS:
Tino NyaweloJohn MatthewsJordan GertonSarah Braden
Computing and computational thinking are integral to the practice of modern science, technology, engineering, and math (STEM); therefore, computational skills are essential for students' preparation to participate in computationally intensive STEM fields and the emerging workforce. In the U.S., Latinx and Spanish speaking students are underrepresented in computing and STEM fields, therefore, expanding opportunities for students to learn computing is an urgent need. The Georgia Institute of Technology and the University of Puerto Rico will collaborate on research and development that will provide Latinx and Spanish speaking students in the continental U.S. and Puerto Rico, opportunities to learn computer science and its application in solving problems in STEM fields. The project will use a creative approach to teaching computer science by engaging Latinx and Spanish speaking students in learning how to code and reprogram in a music platform, EarSketch. The culturally relevant educational practices of the curriculum, as a model for informal STEM learning, will enable students to code and reprogram music, including sounds relevant to their own cultures, community narratives, and cultural storytelling. Research results will inform education programs seeking to design culturally authentic activities for diverse populations as a means to broaden participation in integrated STEM and Computing. This Broad Implementation project 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, including multiple pathways for broadening access to and engagement in STEM learning, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.
As part of the technical innovation of the project, the EarSketch platform will be redesigned for cultural and linguistic authenticity that will include incorporating traditional and contemporary Latin sound beats and musical samples into the software so that students can remix music and learn coding using sounds relevant to their cultures; and developing a Spanish version of the platform, with a toggle to easily switch between English and Spanish. Investigators will also develop an informal STEM curriculum using best practices from Culturally Relevant Education and Cultural Sustaining Pedagogy that provides authentic, culturally and linguistically rich opportunities for student engagement by establishing direct and constant connections to their cultures, communities and lived experiences. The curriculum design and implementation team will work collaboratively with members of Latinx diverse cultural groups to ensure semantic and content equivalency across diverse students and sites. Validating the intervention across students and sites is one of the goals of the project. The model curriculum for informal learning will be implemented as a semester long afterschool program in six schools per year in Atlanta and Puerto Rico, and as a one-week summer camp twice in the summer. The curricular materials will be broadly disseminated, and training will be provided to informal learning practitioners as part of the project. The research will explore differences in musical and computational engagement; the interconnection between music and the computational aspects of EarSketch; and the degree to which the program promotes cultural engagement among culturally and linguistically heterogenous groups of Latinx students in Atlanta, and more culturally and linguistically homogenous Latinx students in Puerto Rico. Investigators will use a mixed method design to collect data from surveys, interviews, focus groups, and computational/musical artifacts created by students. The study will employ multiple case study methodology to analyze and compare the implementation of the critical components of the program in Puerto Rico and Atlanta, and to explore differences in students' musical and computational thinking practices in the two regions. Results from the research will determine the impact of the curriculum on computer science skills and associated computational practices; and contribute to the understanding of the role of cultural engagement on educational outcomes such as sense of belonging, persistence, computational thinking, programming content knowledge and computer science identity. Results will inform education programs designing culturally authentic and engaging programming for diverse populations of Latinx youths.
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TEAM MEMBERS:
Diley HernandezJason FreemanDouglas EdwardsRafael Arce-NazarioJoseph Carroll-Miranda
Youth generate data in the form of social media posts, and they are likely to understand that these data can be used by others for multiple purposes. However, they may be less likely to know that other personal data, such as records related to shopping patterns or medical visits, can also be tracked, analyzed, and used. Consequently, today's young people have a personal stake in their ability to understand and critically question multiple types of data practices. This project will advance knowledge regarding how informal educational organizations can empower young people in a data-centric world. In partnership with public libraries in New York City, Pratt Institute will develop a model for supporting critical data literacy in informal settings. Critical data literacy includes the ability to critique data practices throughout the data life cycle; to situate data within broader contexts such as cyberinfrastructures and societal trends; and to use data to answer questions and to achieve purposes that are personally meaningful and important. To develop a model of informal education that supports critical data literacy, the project team will co-design data literacy sessions with teenagers in libraries. These data literacy sessions will provide teens with opportunities to engage in critical data practices and inquiry in the context of issues they identify as being important to them. The project team will conduct research on the methods that support youths' co-design of critical data literacy programs. This project will result in a model of a youth-driven educational program that can be scaled and enacted in libraries and informal settings nationwide, with the ultimate purpose of fostering a more empowered, data-literate citizenry.
The project will recruit 25 teenagers ages 13-17, including those from underrepresented groups, to co-design and implement four to six 90-minute critical data literacy sessions in a public library. The research team will use design-based participatory research to study the process of co-design, and they will improve this co-design process with three additional cohorts of 25 teenagers each. This study will answer the following three research questions: (1) How can critical data literacy be supported within the sociohistorical context of the public library in ways that speak to young people? (2) How can the affordances of co-design scaffold meaningful informal learning about critical data literacy? (3) What do the designs and artifacts created by young people say about sustained engagement and learning with regard to facets of critical data literacy? To answer these questions, the research team will use thematic and descriptive coding to analyze data sources such as interviews and focus groups with teens and library staff, observations of critical data literacy sessions, youth-generated artifacts, and surveys with youth participants. Empirical findings will be disseminated widely through professional networks, conferences, and journals for informal educators, educational researchers, and information scientists, and the co-design model will be disseminated widely to practitioners of informal science education. This project is funded by the Advanced Informal STEM Learning (AISL) program. As part of its overall strategy to enhance learning in informal environments, the 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.
This Pilot and Feasability Study award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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TEAM MEMBERS:
Leanne BowlerMark RosinIrene Lopatovska
Despite the ubiquity of Artificial Intelligence (AI), public understanding of how it works and is used is limited This project will research, design, and develop innovative approaches focusing on Artificial Intelligence (AI) for under-represented youth ages 14-24. Program components include live social media chats with AI leaders, app development, journalistic investigations of ethical issues in machine learning, and review of AI-based consumer products. Youth Radio is a non-profit media and tech organizations that provides youth with skills in STEM, journalism, arts, and communications. They engage 250 youth annually through free after-school classes and work shifts. Participants are 90% youth of color and 80% low income. Project partners include the MIT Media Lab which developed App Inventor which allows novice users to build fully functional apps. Staff from Google will serve as a project advisor on the curriculum. The project has exceptional national reach through the dissemination of its media and apps through national outlets such as NPR and Teen Vogue as well as various platforms including online, on-air, as well as presentations, publications, and training tools. The project broadens participation by engaging these low income youth of color in developing skills critical to the workforce of the future. It will help prepare an upcoming generation of Artificial Intelligence creators, users, and consumers who understand the technology and embrace and encourage its potential.It will give them the necessary knowledge and opportunities for careers in an AI-driven future.
This project is grounded in sociocultural learning theory and practice and is interdisciplinary by design. The theoretical framework holds that Computational Thinking plus Critical Pedagogy leads to Critical Computational Literacy. Also, Digital Age Civics plus Participatory Culture leads to Civic Imagination helping youth build a better world through technology. The driving research questions include: What do underrepresented youth understand about AI and its role in society? What are the ethical dilemmas posed by AI from their vantage point? What are the features of an engaging ethics-centered pedagogy with AI? What impact do the AI products developed by the youth have on the target audience? The research design will use ethnographic techniques and design research to study and analyze youth learning. Data sources will include baseline surveys, audio recordings and transcriptions from learning sessions with the participants, research analytic memos, focus group interviews, student-generating artifacts of learning and finished products, etc. The design-based approach will enable systematic, evidence-based iteration on the initiative's activities, pedagogical approach and products. An independent summative evaluation will provide complementary data and perspective to triangulate with the research findings.
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:
Elisabeth SoepEllin O'LearyHarold Abelson
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