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
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?
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
Craft has emerged as an important reference point for human-computer interaction (HCI). To avoid a misrepresenting, all-encompassing application of craft to interaction design, this position paper first discerns craft from HCI. It develops material engagement and mediation as differentiating factors to reposition craft in relation to tangible interaction design. The aim is to clarify craft’s relation to interaction design and to open up new opportunities and questions that follow from this repositioning.
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
In partnership with the Digital NEST, students engage in near to peer learning with a technical tool for the benefit of a nonprofit that tackles issues the youth are passionate about. Youth build first from an 'internal’ Impactathon, to planning and developing an additional Impactathon for a local partner and then traveling to another partner elsewhere in the state. Participants range from 14 to 24 from UC Santa Cruz students to middle schoolers from Watsonville and Salinas.
This poster was presented at the 2019 AISL Principal Investigators Meeting.
This Research in Service to Practice 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. 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 project will research the educational impact of social robots in informal learning environments, with applications to how social robots can improve participation and engagement of middle-school girls in out-of-school computer science programs in under-resourced rural and urban areas. The use of robots to improve STEM outcomes has focused on having learners program robots as tools to accomplish tasks (e.g., play soccer). An alternate approach views robots as social actors that can respond intelligently to users. By designing a programmable robot with social characteristics, the project aims to create a culturally-responsive curriculum for Latina, African American, and Native American girls who have been excluded by approaches that separate technical skill and social interaction. The knowledge produced by this project related to the use and benefits of social programmable robots has the potential to impact the many after-school and weekend programs that attempt to engage learners in STEM ideas using programmable robot curricula.
The project robot, named Cozmo, will be programmed using a visual programming language and will convey emotion with facial expressions, sounds, and movements. Middle school girls will engage in programming activities, collaborative reflection, and interact with college women mentors trained to facilitate the course. The project will investigate whether the socially expressive Cozmo improves computer science outcomes such as attitudes, self-efficacy, and knowledge among the middle school female participants differently than the non-social version. The project will also investigate whether adding rapport-building dialogue to Cozmo enhances these outcomes (e.g., when a learner succeeds in getting Cozmo to move, Cozmo can celebrate, saying "I can move! You're amazing!"). These questions will be examined research conducted with participants in multi-session after-school courses facilitated by Girl Scout troops in Arizona. The project will disseminate project research and resources widely by sharing research findings in educational and learning science journals; creating a website with open source code for programming social robots; and making project curriculum and related guidelines available to Girl Scouts and other educational programs.
This 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.
The FIRST Longitudinal Study is a multi-year longitudinal study assessing the impacts of FIRST’s afterschool robotics programs on the STEM related interests and educational and career trajectories of program participants. FIRST is one of the nation’s largest after-school robotics programs, serving more than 460,000 youth aged 6-18 annually through the FIRST LEGO League (Ages 7-14), the FIRST Tech Challenge (grades 7-12) and the FIRST Robotics Competition (grades 9-12). The study is tracking over 1200 program participants and comparison students, using a quasi-experimental design, over a
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TEAM MEMBERS:
Alan MelchiorCathy BurackMatthew HooverJill Marcus