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.
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 guide describes what took place during NYSCI’s Big Data for Little Kids workshop series, Museum Makers: Designing With Data. In addition to detailed outlines of the activities implemented during the program, this guide includes a glossary of recurrent terms and resources used throughout the workshops.
In 2017, as part of a National Science Foundation funded project, the New York Hall of Science (NYSCI) set out to teach Big Data concepts to children ages 4 – 8 years old. NYSCI developed and piloted an after-school program for families to utilize the data cycle as a method of informed
In this literature review, we seek to understand in what ways aspects of computer science education and making and makerspaces may support the ambitious vision for science education put forth in A Framework for K-12 Science as carried forward in the Next Generation Science Standards. Specifically, we examine how computer science and making and makerspace approaches may inform a project-based learning approach for supporting three-dimensional science learning at the elementary level. We reviewed the methods and findings of both recently published articles by influential scholars in computer
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TEAM MEMBERS:
Samuel SeveranceSusan CodereEmily MillerDeborah Peek-BrownJoseph Krajcik
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
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project brings together two approaches to help K-12 students learn programming and computer science: open-ended learning environments, and computer-based learning analytics, to help create a setting where youth can get help and scaffolding tailored to what they know about programming without having to take tests or participate in rigid textbook exercises for the system to know what they know.
The project proposes to use techniques from educational data mining and learning analytics to process student data in the Alice programming environment. Building on the assessment design model of Evidence-Centered Design, student log data will be used to construct a model of individual students' computational thinking practices, aligned with emerging standards including NGSS and research on assessment of computational thinking. Initially, the system will be developed based on an existing corpus of pair-programming log data from approximately 600 students, triangulating with manually-coded performance assessments of programming through game design exercises. In the second phase of the work, curricula and professional development will be created to allow the system to be tested with underrepresented girls at Stanford's CS summer workshops and with students from diverse high schools implementing the Exploring Computer Science curriculum. Direct observation and interviews will be used to improve the model. Research will address how learners enact computational thinking practices in building computational artifacts, what patters of behavior serve as evidence of learning CT practices, and how to better design constructionist programming environments so that personalized learner scaffolding can be provided. By aligning with a popular programming environment (Alice) and a widely-used computer science curriculum (Exploring Computer Science), the project can have broad impact on computer science education; software developed will be released under a BSD-style license so others can build on it.
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TEAM MEMBERS:
Shuchi GroverMarie BienkowskiJohn Stamper
Increasingly, the prosperity, innovation and security of individuals and communities depend on a big data literate society. Yet conspicuously absent from the big data revolution is the field of teaching and learning. The revolution in big data must match a complementary revolution in a new kind of literacy, through a significant infusion of STEM education with the kinds of skills that the revolution in 21st century data-driven science demands. This project represents a concerted effort to determine what it means to be a big data literate citizen, information worker, researcher, or policymaker; to identify the quality of learning resources and programs to improve big data literacy; and to chart a path forward that will bridge big data practice with big data learning, education and career readiness.
Through a process of inquiry research and capacity-building, New York Hall of Science will bring together experts from member institutions of the Northeast Big Data Innovation Hub to galvanize big data communities of practice around education, identify and articulate the nature and quality of extant big data education resources and draft a set of big data literacy principles. The results of this planning process will be a planning document for a Big Data Literacy Spoke that will form an initiative to develop frameworks, strategies and scope and sequence to advance lifelong big data literacy for grades P-20 and across learning settings; and devise, implement, and evaluate programs, curricula and interventions to improve big data literacy for all. The planning document will articulate the findings of the inquiry research and evaluation to provide a practical tool to inform and cultivate other initiatives in data literacy both within the Northeast Big Data Innovation Hub and beyond.
The Science Museum of Minnesota (SMM) leverages a professional educator team (“instructors”) comprised of about two dozen individuals who facilitate both formal and informal educational programming in the museum, in K–12 classrooms, and at community-based sites. The experienced instructors of SMM’s Lifelong Learning Group bring innovative programs to both students and their teachers. Recognizing that long-term experiences can have a profound impact on students and teachers, SMM works to develop multiyear relationships based on collaboration. This article focuses primarily on SMM’s well
Lack of diversity in science and engineering education has contributed to significant inequality in a workforce that is responsible for addressing today's grand challenges. Broadening participation in these fields will promote the progress of science and advance national health, prosperity and welfare, as well as secure the national defense; however, students from underrepresented groups, including women, report different experiences than the majority of students, even within the same fields. These distinctions are not caused by the students' ability, but rather by insufficient aspiration, confidence, mentorship, instructional methods, and connection and relevance to their cultural identity. The long-term vision of this project is to amplify the impact of a successful broadening participation model at the University of Maine, the Stormwater Research Management Team (SMART). This program trains students and mentors in using science and engineering skills and technology to research water quality in their local watershed. Students engage in numerous science and technology fields: engineering design, data acquisition, analysis and visualization, chemistry, environmental science, biology, and information technology. Students also connect with a diversity of professionals in water and engineering in government, private firms and non-profits. SMART has augmented the traditional science and engineering classroom by engaging students in guided mentored apprenticeships that address community problems.
Technical
This pilot project will form a collaborative and define a strategic plan for scale-up to a national alliance to increase the long-term success rate of underrepresented minority students in science, engineering, and related fields. The collaborative of multiple and varied organizations will align to collectively contribute time and resources to a pre-college educational pathway. There are countless isolated programs that offer short-term interventions for underrepresented and minority students; however, there is lack of organizational coordination for aligning current program offerings, sharing best practices, research results or program outcomes along the education to workforce pathway. The collaborative activities will focus on the transition grades (e.g., 4-5, 8, and high school) and emphasize relationships among skills, confidence, culture and future careers. Collaborative partners will establish a centralized infrastructure in each location to coordinate recruiting of invested community leaders, educators, and parents, around a common agenda by designing, deploying and continually assessing a stormwater-themed project that addresses their location and demographic specific needs. This collaborative community will consist of higher education faculty and students, K-12 students, their caregivers, mentors, educators, stormwater districts, state and national environmental protection agencies, departments of education, and other for-profit and non-profit organizations. The collaborative will address the need for research on mechanisms for change, collaboration, and negotiation regarding the greater participation of under-represented groups in the science and technology workforce.
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TEAM MEMBERS:
Mohamed MusaviVenkat BhethanabotlaCary JamesVemitra WhiteLola Brown
The Colleges of Science & Engineering and Graduate Education, and the Metro Academies College Success Program (Metro) at San Francisco State University in partnership with San Francisco Unified School District and the San Francisco Chamber of Commerce develop an integrated approach for computing education that overcomes obstacles hampering broader participation in the U.S. science, technology, engineering and mathematics (STEM) workforce. The partnership fosters a more diverse and computing-proficient STEM workforce by establishing an inclusive education approach in computer science (CS), information technology, and computer engineering that keeps students at all levels engaged and successful in computing and graduates them STEM career-ready.
Utilizing the collective impact framework maximizes the efficacy of existing regional organizations to broaden participation of groups under-educated in computing. The collective impact model establishes a rich context for organizational engagement in inclusive teaching and learning of CS. The combination of the collective impact model of social agency and direct engagements with communities yields unique insights into the views and experiences of the target population of students and serves as a platform for national scalable networks.
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TEAM MEMBERS:
Keith BowmanIlmi YoonLarry HorvathEric HsuJames Ryan