In this collaborative project, a university research lab and children's science museum work together to design, implement, study, and revise a week-long data science camp for middle school age students, data science learning assessment items and a facilitator training curriculum.
Arecibo C3 will serve as a collaborative hub for STEM discovery and exploration by building upon existing programs and opportunities established at the Arecibo site by previous NSF programs, while also creating new STEM education, research, and outreach programs and initiatives. The goals for the Center are to (1) promote STEM education, learning, and teaching; (2) support fundamental and applied STEM and STEM education research; (3) broaden participation in STEM; and (4) build and strengthen collaborations and partnerships.
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TEAM MEMBERS:
Jose Agosto RiveraJoseph Carroll-MirandaJaime Abreu RamosAmilcar VelezJason WilliamsCristina Fernandez-MarcoWanda Diaz MercedAnuchka RamosPatricia Ordonez
This project seeks to apply explainable artificial intelligence to the challenge of personalizing training for adult citizen scientists. The approach will be developed in the context of the Native Bee Watch (NBW) biodiversity monitoring project that began in 2016 at Colorado State University.
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TEAM MEMBERS:
Sarath SreedharanNikhil KrishnaswamyJill ZarestkyNathaniel Blanchard
Structural inequities contribute to the disproportionate incarceration of Black and African American women, as well as women from the working class. This project will work toward redressing these inequities through developing and researching an ecosystem designed to support formerly incarcerated women's transition into careers that require technology-based skills or computational thinking.
Mathematizing, Visualizing, and Power (MVP): Appalachian Youth Becoming Data Artists for Community Learning is a three-year Advancing Informal STEM Learning, Innovations and Development, project that focuses on community-centered data exploration catalyzed by youth. The project develops statistical artistry among young people in East Tennessee Appalachian communities and enables these youth to share their data visualizations with their communities to foster collective reflection and understanding. The creative work generated by the MVP project will be compelling in two ways, both as statistical art and as powerful statements giving voice to the experience of communities. Critical aspects of the MVP model include (1) youth learning sessions that position youth as owners of data and producers of knowledge and (2) Community Learning Events that support community learning as youth learning occurs. The MVP project has a primary focus on broadening the STEM participation of underrepresented communities of Appalachia. The project’s mission is to increase the learning and life outcomes of young people and communities of Appalachia by creating a meaningful foundation of data science and collective data exploration. The University of Tennessee partners with Pellissippi State Community College, Drexel University, and the Boys & Girls Club of the Tennessee Valley to bring together a convergent team of community members, practitioners, and professionals, with the expertise to carry out the project. The project will impact approximately 120 youth and 3800 of their East Tennessee community members. The research generated will inform how to engage community members in learning about community issues through the exploration of datasets relevant to participants.
The field of STEM education is in urgent need of knowledge about effective models to inspire community-based data exploration with young people as leaders in these efforts. The MVP project includes engaging youth with meaningful problems, building a discourse community with possibilities for action, re-positioning youth as knowledge producers within their own communities, leveraging linguistic and cultural resources of the youth participants and their communities, and implementing critical events that support substantial interaction between youth, community members, and the data visualizations. MVP builds on the idea that the design of data visualizations requires an understanding of both data science and artistic design. Research will inform the model of community engagement, examine data artists’ identities, and document community learning. The MVP model will be designed, developed, tested, and refined through three cycles of design-based research. The overarching research question guiding these cycles is: What affordances (and delimitations) related to identity and learning does the model provide for MVP Youth and community members? Data sources for the project include: fieldnotes, portfolios created by MVP Youth, youth pre/post interviews, observations of the learning sessions, a project documentary, surveys for youth and community members, interviews with community members, and audience feedback. The National Institute for STEM Evaluation and Research (NISER) will provide formative and summative evaluation about project activities. Formative feedback will be integrated into the ongoing research cycles. The research conducted will inform (1) the community learning model; (2) the integrated pedagogy and curriculum of the MVP Youth learning sessions that emphasize data science through design arts; and, (3) research on community learning and youth identity. Findings will be shared through conferences, academic and practitioner-focused journals, a video documentary, a Summit on Engaging Youth and Communities in Data, and a project website.
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TEAM MEMBERS:
Lynn HodgeElizabeth DyerJoy BertlingCarlye Clark
Data is increasingly important in all aspects of people’s lives, from the day-to-day, to careers and to civic engagement. Preparing youth to use data to answer questions and solve problems empowers them to participate in society as informed citizens and opens doors to 21st century career opportunities. Ensuring equitable representation in data literacy and data science careers is critical. For many girls underrepresented in STEM, developing a "data science identity" requires personally meaningful experiences working with data. This project aims to promote middle school-aged girls’ interest and aspirations in data science through an identity-aligned, social game-based learning approach. The goals are to create a more diverse and inclusive generation of data scientists who see data as a resource and who are equipped with the skills and dispositions necessary to work with data in order to solve practical problems. The research team will run 10 social clubs and 10 data science clubs mentored by women in data science recruited through the University of Miami’s Institute for Data Science and Computing. Participants will be 250 middle school-aged girls recruited in Miami, FL, and Yolo County, CA, through local and national girls’ organizations. Youth will participate in a data science club and will learn key data science concepts and skills, including data structures, storage, exploration, analysis, and visualization. These concepts will be learned from working with their own data collected in personally meaningful ways in addition to working with data collected by others in the same social game eco-system. The project will also develop facilitator materials to allow adult volunteers to create game-based informal data science learning experiences for youth in their areas. The 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 and is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST), which seeks to engage underrepresented students in technology-rich learning environments, including skills in data literacy, and increase students’ knowledge and interest in information and communication technology (ICT) careers.
Researchers will focus on two primary research questions: 1) Across gameplay and club experiences, in what ways do participants engage with data to pursue personal or social goals? 2) How do gameplay and club experiences shape girls’ perceptions of data, data science, and their fit with data and data science? The project will use design-based research methods to iteratively design the game and social club experiences. To ensure that uses of data feel personally and socially meaningful to young girls, the virtual world’s goals, narratives, and activities will be co-designed with girls from groups underrepresented in data science. The project will research engagement with game data in two informal, game-based learning scenarios: organic, self-directed, social play club, and structured, adult-facilitated data science clubs. The research will use a combination of quantitative and qualitative methods including surveys, focus groups, interviews, and gameplay and club observations. Project evaluation will determine how gameplay and club experiences impact participants' attitudes toward and interest in data-rich futures. The project holds the potential for broadening participation and promoting interest in data science by blending game-based learning with the rich social and adult mentoring through club participation. The results will be disseminated through conference presentations, scholarly publications, and social media. The game and facilitator materials will be designed for dissemination and made freely available to the public.
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TEAM MEMBERS:
Lisa HardyGary GoldbergerJennifer Kahn
Artificial intelligence (AI) is in many of our everyday activities—from unlocking phones to running Internet searches to parking cars. Yet, most instruction on how AI works is only in computer science courses. The unique role that AI plays in making decisions that affect human lives heightens the need for education approaches that promote public AI literacy. Little research has been done to understand how we can best teach AI in informal learning spaces. This project will engage middle school age youth in learning abouts AI through interaction with museum exhibits in science and technology centers. The exhibits employ embodied interactions and creative making activities that involve textiles, music making, and interactive media. The research will build on three exhibit prototypes that teach about concepts including bias in data in machine learning, AI decision-making processes, and how AI represents knowledge. Female-identifying and Title 1 youth will be recruited as participants during the exhibit design iterations and testing. The 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
Researchers will explore two key research questions: 1) How can the design of interactive museum exhibits encourage interest development in and learning about AI among learners without a Computer Science background by using embodiment and creative making? and 2) How do embodied interaction and creative making mediate learning about AI in informal learning environments? The project will take a design-based research approach, iteratively building on existing exhibit prototypes and testing them in-situ with learners. Data sources and modes of analysis will include retrospective surveys to assess interest, content knowledge gain, creativity, learning talk analysis of audio recordings, and coding of embodied movements in video recordings. Learning talk analysis will identify instances of joint sensemaking during naturalistic interactions with our exhibit to reveal connections between sensemaking talk; learners' behaviors and embodied actions during real-time collaborative knowledge building; and outcomes in knowledge, interest, and creativity measures as elicited in retrospective surveys. The final set of exhibits will be rigorously evaluated with over 500 museum visitors. The key contributions of this work will include a set of rigorously tested exhibits, publicly available exhibit designs, a set of design guidelines for developing AI literacy museum exhibits, and an improved understanding of the relationship between AI-related learning and interest development, embodiment, and creativity.
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TEAM MEMBERS:
Brian MagerkoDuri LongJessica Roberts
This project is expanding an effective mobile making program to achieve sustainable, widespread impact among underserved youth. Making is a design-based, participant-driven endeavor that is based on a learning by doing pedagogy. For nearly a decade, California State University San Marcos has operated out-of-school making programs for bringing both equipment and university student facilitators to the sites in under-served communities. In collaboration with four other CSU campuses, this project will expand along four dimensions: (a) adding community sites in addition to school sites (b) adding rural contexts in addition to urban/suburban, (c) adding hybrid and online options in addition to in-person), and (d) including future teachers as facilitators in addition to STEM undergraduates. The program uses design thinking as a framework to engage participants in addressing real-world problems that are personally and socially meaningful. Participants will use low- and high-tech tools, such as circuity, coding, and robotics to engage in activities that respond to design challenges. A diverse group of university students will lead weekly, 90-minute activities and serve as near-peer mentors, providing a connection to the university for the youth participants, many of whom will be first-generation college students. The project will significantly expand the Mobile Making program from 12 sites in North San Diego County to 48 sites across California, with nearly 2,000 university facilitators providing 12 hours of programming each year to over 10,000 underserved youth (grades 4th through 8th) during the five-year timeline.
The project research will examine whether the additional sites and program variations result in positive youth and university student outcomes. For youth in grades 4 through 8, the project will evaluate impacts including sustained interest in making and STEM, increased self-efficacy in making and STEM, and a greater sense that making and STEM are relevant to their lives. For university student facilitators, the project will investigate impacts including broadened technical skills, increased leadership and 21st century skills, and increased lifelong interest in STEM outreach/informal science education. Multiple sources of data will be used to research the expanded Mobile Making program's impact on youth and undergraduate participants, compare implementation sites, and understand the program's efficacy when across different communities with diverse learner populations. A mixed methods approach that leverages extant data (attendance numbers, student artifacts), surveys, focus groups, making session feedback forms, observations, and field notes will together be used to assess youth and university student participant outcomes. The project will disaggregate data based on gender, race/ethnicity, grade level, and site to understand the Mobile Making program's impact on youth participants at multiple levels across contexts. The project will further compare findings from different types of implementation sites (e.g., school vs. library), learner groups, (e.g., middle vs. upper elementary students), and facilitator groups (e.g., STEM majors vs. future teachers). This will enable the project to conduct cross-case comparisons between CSU campuses. Project research will also compare findings from urban and rural school sites as well as based on the modality of teaching and learning (e.g., in-person vs. online). The mobile making program activities, project research, and a toolkit for implementing a Mobile maker program will be widely disseminated to researchers, educators, and out-of-school programs.