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 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?
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
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
This is an Early-concept Grant for Exploratory Research supporting research in Smart and Connected Communities. The research supported by the award is collaborative with research at the University of Colorado. The researchers are studying the use of technologies to enable communities to connect youth and youth organizations to effectively support diverse learning pathways for all students. These communities, the youth, the youth organizations, formal and informal education organizations, and civic organizations form a learning ecology. The DePaul University researchers will design and implement a smart community infrastructure in the City of Chicago to track real-time student participation in community STEM activities and to develop mobile applications for both students and adults. The smart community infrastructure will bring together information from a variety of sources that affect students' participation in community activities. These include geographic information (e.g., where the student lives, where the activities take place, the student transportation options, the school the student attends), student related information (e.g., the education and experience background of the student, the economic status of the student, students' schedules), and activity information (e.g., location of activity, requirements for participation). The University of Colorado researchers will take the lead on analyzing these data in terms of a community learning ecologies framework and will explore computational approaches (i.e., recommender systems, visualizations of learning opportunities) to improve youth exploration and uptake of interests and programs. These smart technologies are then used to reduce the friction in the learning connection infrastructure (called L3 for informal, formal, and virtual learning) to enable the student to access opportunities for participation in STEM activities that are most feasible and most appropriate for the student. Such a flexible computational approach is needed to support the necessary diversity of potential recommendations: new interests for youth to explore; specific programs based on interests, friends' activities, or geographic accessibility; or programs needed to "level-up" (develop deeper skills) and complete skills to enhance youths' learning portfolios. Although this information was always available, it was never integrated so it could be used to serve the community of both learners and the providers and to provide measurable student learning and participation outcomes. The learning ecologies theoretical framework and supporting computational methods are a contribution to the state of the art in studying afterschool learning opportunities. While the concept of learning ecologies is not new, to date, no one has offered such a systematic and theoretically-grounded portfolio of measures for characterizing the health and resilience of STEM learning ecologies at multiple scales. The theoretical frameworks and concepts draw together multiple research and application domains: computer science, sociology of education, complexity science, and urban planning. The L3 Connects infrastructure itself represents an unprecedented opportunities for conducting "living lab" experiments to improve stakeholder experience of linking providers to a single network and linking youth to more expanded and varied opportunities. The University of Colorado team will employ three methods: mapping, modeling, and linking youth to STEM learning opportunities in school and out of school settings in a large urban city (Chicago). The recommender system will be embedded into youth and parent facing mobile apps, enabling the team to characterize the degree to which content-based, collaborative filtering, or constraint based recommendations influence youth actions. The project will result in two measurable outcomes of importance to key L3 stakeholder groups: a 10% increase in the number of providers (programs that are part of the infrastructure) in target neighborhoods and a 20% increase in the number of youth participating in programs.
This is an Early-concept Grant for Exploratory Research supporting research in Smart and Connected Communities. The research supported by the award is collaborative with research at DePaul University. The researchers are studying the use of technologies to enable communities to connect youth and youth organizations to effectively support diverse learning pathways for all students. These communities, the youth, the youth organizations, formal and informal education organizations, and civic organizations form a learning ecology. The DePaul University researchers will design and implement a smart community infrastructure in the City of Chicago to track real-time student participation in community STEM activities and to develop mobile applications for both students and adults. The smart community infrastructure will bring together information from a variety of sources that affect students' participation in community activities. These include geographic information (e.g., where the student lives, where the activities take place, the student transportation options, the school the student attends), student related information (e.g., the education and experience background of the student, the economic status of the student, students' schedules), and activity information (e.g., location of activity, requirements for participation). The University of Colorado researchers will take the lead on analyzing these data in terms of a community learning ecologies framework and will explore computational approaches (i.e., recommender systems, visualizations of learning opportunities) to improve youth exploration and uptake of interests and programs. These smart technologies are then used to reduce the friction in the learning connection infrastructure (called L3 for informal, formal, and virtual learning) to enable the student to access opportunities for participation in STEM activities that are most feasible and most appropriate for the student. Such a flexible computational approach is needed to support the necessary diversity of potential recommendations: new interests for youth to explore; specific programs based on interests, friends' activities, or geographic accessibility; or programs needed to "level-up" (develop deeper skills) and complete skills to enhance youths' learning portfolios. Although this information was always available, it was never integrated so it could be used to serve the community of both learners and the providers and to provide measurable student learning and participation outcomes. The learning ecologies theoretical framework and supporting computational methods are a contribution to the state of the art in studying afterschool learning opportunities. While the concept of learning ecologies is not new, to date, no one has offered such a systematic and theoretically-grounded portfolio of measures for characterizing the health and resilience of STEM learning ecologies at multiple scales. The theoretical frameworks and concepts draw together multiple research and application domains: computer science, sociology of education, complexity science, and urban planning. The L3 Connects infrastructure itself represents an unprecedented opportunities for conducting "living lab" experiments to improve stakeholder experience of linking providers to a single network and linking youth to more expanded and varied opportunities. The University of Colorado team will employ three methods: mapping, modeling, and linking youth to STEM learning opportunities in school and out of school settings in a large urban city (Chicago). The recommender system will be embedded into youth and parent facing mobile apps, enabling the team to characterize the degree to which content-based, collaborative filtering, or constraint based recommendations influence youth actions. The project will result in two measurable outcomes of importance to key L3 stakeholder groups: a 10% increase in the number of providers (programs that are part of the infrastructure) in target neighborhoods and a 20% increase in the number of youth participating in programs.
This Research in Service to Practice project, a collaboration of Pepperdine University and the New York Hall of Science, will establish a network of STEM-related Media Making Clubs comprised of after-school students aged 12 - 19 and teachers in the U.S. and in three other countries: Kenya, Namibia and Finland. The media produced by the students may include a range of formats such as videos, short subject films, games, computer programs and specialized applications like interactive books. The content of the media produced by the students will focus on the illustration and teaching of STEM topics, where the shared media is intended to help other students become enthused about and learn the science. This proposal builds on the principal investigator's previous work on localized media clubs by now creating an international network in which after-school students and teachers will collaborate at a distance with other clubs. The central research questions for the project pertain to three themes at the intersection of learning, culture and collaboration: the impact of participatory teaching, virtual networks, and intercultural, global competence. The research will combine qualitative, cross-cultural and big data methods. Critical to the innovation of the project, the research team will also develop a network assessment tool, adapting epistemic network analysis methods to the needs of this initiative. This work 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.
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TEAM MEMBERS:
Eric HamiltonKatherine McMillanPriya Mohabir
As part of an overall strategy to enhance learning within maker contexts in formal and informal environments, the Innovative Technology Experiences for Students and Teachers (ITEST) and Advancing Informal STEM Learning (AISL) programs partnered to support innovative models for making in a variety of settings through the Enabling the Future of Making to Catalyze New Approaches in STEM Learning and Innovation Dear Colleague Letter. This Early Concept Grant for Exploratory Research (EAGER) will test an innovative approach to bringing making from primarily informal out-of-school contexts into formal science classrooms. While the literature base to support the positive outcomes and impacts of design-based making in informal settings at the K-12 level is emerging, to date, minimal studies have investigated the impacts of making design principles within formal contexts. If successful, this project would not only add to this gap in the literature base but would also present a novel model for bridging the successful engineering design practices of making and tinkering primarily found in informal science education into formal science education classrooms. The model would also demonstrate an innovative, highly interactive way to engage high school students and their teachers in engineering based design principles with immediate real-world applications, as the scientific instruments developed in this project could be integrated directly into science classrooms at relatively minimal costs.
Through a multi-phased design and implementation model, high school students and their teachers will engage deeply in making design principles through the design and development of their own scientific instruments using Arduino-compatible hardware and software. The first phase of the project will reflect a more traditional making experience with up to twenty high school students and their teachers participating in an after-school design making club, in this case, focused on the development and testing of scientific instrument prototypes. During the second phase of the project, the first effort to transpose the after school making experience to a more formalized experience will be tested with up to eight students selected to participate in two week summer research internships focused on scientific instrument design and development through making at Northwestern University. A two-day summer teacher workshop will also be held for high school teachers participating in the subsequent pilot study. The collective insights gleaned from the after school program, student internships, and teacher workshop will culminate to inform the full implementation of the formal classroom pilot study. The third and final phase will coalesce months of iterative, formative research, design and development, resulting in a comprehensive pilot investigation in up to seven high school physics classrooms.
Using a multi-phased, mixed methods exploratory design-based research approach, this 18-month EAGER will explore several salient research questions: (a) How and to what extent does the design & making of scientific instrumentation serve as useful tasks for learning important science and engineering knowledge, practices, and epistemologies? (b) How engaging is this making activity to learners of diverse abilities and prior interests? What can be generalized to other types of making activities? (c) How accessible is the Arduino hardware and coding environment to learners? What combination of hardware and software materials and tools best support accessibility and learning in this type of digital making activity? and (d) What types of scaffolding (for students and teachers) are required to support the effective use of maker materials and activities in a classroom setting? Structured interviews, artifacts, video recordings from visor cameras, student design logs, logfiles, and ethnographic field notes will be employed to garner data and address the research questions. Given the early stage of the proposed research, the dissemination of the findings will be limited to a few select journals, teacher forums and workshops, and professional conferences.
This EAGER is well-poised to directly impact up to 125 high school physics students (average= 25 students/class), approximately 7 high school physics teachers, 6-8 high school summer interns, nearly 20 high school students participating in the after-school design making club, and indirectly many more. The results of this EAGER could provide the basis and evidence needed to support a more robust, expanded future investigation to further substantiate the findings and build the case for similar efforts to bring making into formal science education contexts.
Rockman et al (REA), a San Francisco-based research and evaluation firm, conducted the external evaluation for Youth Radio's DO IT! program, which was funded by the National Science Foundation. Building upon Youth Radio's previous Science and Technology Program, the DO IT! initiative consisted of three primary components that promoted STEM (science, technology, engineering, and mathematics) learning by training underserved youth in cutting-edge digital technologies: (1) Brains and Beakers: Young people hosted a line-up of investigators and inventors for demo-dialogues at Youth Radio's studios
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TEAM MEMBERS:
Rockman et al | Youth RadioKristin BassJulia Hazer
The University of Massachusetts Lowell and Machine Science Inc. propose to develop and to design an on-line learning system that enables schools and community centers to support IT-intensive engineering design programs for students in grades 7 to 12. The Internet Community of Design Engineers (iCODE) incorporates step-by-step design plans for IT-intensive, computer-controlled projects, on-line tools for programming microcontrollers, resources to facilitate on-line mentoring by university students and IT professionals, forums for sharing project ideas and engaging in collaborative troubleshooting, and tools for creating web-based project portfolios. The iCODE system will serve more than 175 students from Boston and Lowell over a three-year period. Each participating student attends 25 weekly after-school sessions, two career events, two design exhibitions/competitions, and a week-long summer camp on a University of Massachusetts campus in Boston or Lowell. Throughout the year, students have opportunities to engage in IT-intensive, hands-on activities, using microcontroller kits that have been developed and classroom-tested by University of Massachusetts-Lowell and Machine Science, Inc. About one-third of the participants stay involved for two years, with a small group returning for all three years. One main component for this project is the Handy Cricket which is a microcontroller kit that can be used for sensing, control, data collection, and automation. Programmed in Logo, the Handy Cricket provides an introduction to microcontroller-based projects, suitable for students in grades 7 to 9. Machine Science offers more advanced kits, where students build electronic circuits from their basic components and then write microcontroller code in the C programming language. Machine Science offers more advanced kits, which challenge students to build electronic circuits from their basic components and then write microcontroller code in the C programming language. Machine Science's kits are intended for students in grades 9 to 12. Microcontroller technology is an unseen but pervasive part of everyday life, integrated into virtually all automobiles, home appliances, and electronic devices. Since microcontroller projects result in physical creations, they provide an engaging context for students to develop design and programming skills. Moreover, these projects foster abilities that are critical for success in IT careers, requiring creativity, analytical thinking, and teamwork-not just basic IT skills.
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TEAM MEMBERS:
Fred MartinDouglas PrimeMichelle Scribner-MacLeanSamuel Christy