Given the growth of technology in the 21st century and the growing demands for computer science skills, computational thinking has been increasingly included in K-12 STEM (Science, Technology, Engineering and Mathematics) education. Computational thinking (CT) is relevant to integrated STEM and has many common practices with other STEM disciplines. Previous studies have shown synergies between CT and engineering learning. In addition, many researchers believe that the more children are exposed to CT learning experiences, the stronger their programming abilities will be. As programming is a
Computational Thinking (CT) is a relatively new educational focus and a clear need for learners as a 21st century skill. This proposal tackles this challenging new area for young learners, an area greatly in need of research and learning materials. The Principal Investigators will develop and implement integrated STEM+C museum exhibits and integrate CT in their existing engineering design based PictureSTEM curriculum for K-2 students. They will also pilot assessments of the CT components of the PictureSTEM curriculum. This work will make a unique contribution to the available STEM+C learning materials and assessments. There are few such materials for the kindergarten to second grade (K-2) population they will work with. They will research the effects of the curriculum and the exhibits with a mixed methods approach. First, they will collect observational data and conduct case studies to discover the important elements of an integrated STEM+C experience in both the formal in-school setting with the curriculum and in the informal out-of-school setting with families interacting with the museum exhibits. This work will provide a novel way to understand the important question of how in- and out-of-school experiences contribute to the development of STEM and CT thinking and learning. Finally, they will collect data from all participants to discover the ways that their activities lead to increases in STEM+C knowledge and interest.
The Principal Investigators will build on an integrated STEM curriculum by integrating CT and develop integrated museum exhibits. They base both activities on engineering design implemented through challenge based programming activities. They will research and/or develop assessments of both STEM+C integrated thinking and CT. Their research strategy combines Design Based Research and quantitative assessment of the effectiveness of the materials for learning CT. In the first two years of their study, they will engage in iterations on the design of the curriculum and the exhibits based on observation and case-study data. There will be 16 cases that draw from each grade level and involve data collection for the case student in both schools and museums. They will also use this work to illuminate what integrated STEM+C thinking and learning looks like across formal and informal learning environments. Based in some part on what they discover in this first phase, they will conduct the quantitative assessments with all (or at least most) students participating in the study
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
Reconceptualizing STEM + Computing Literacy is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance multidisciplinary integration of computing and computational thinking in K-12 science, technology, engineering, and mathematics (STEM) teaching and learning through applied research and development across one or more domains, and broadening participation in computing and computing-related fields. The project will study the integration of computational thinking as part of a new and more contemporary perspective of STEM literacy, and will design, develop, and beta-test a prototype literacy assessment tool that will measure computational thinking literacy along with measures of literacy in other STEM content areas. The tool will be available to the general public as a self-measurement application (App) that can be used by individuals to test their own literacy, and by teachers, schools, and informal educators and organizations to assess literacy development in their students and in their STEM education programs. This transdisciplinary research project will begin the process of creating an innovative approach and tool for measuring literacy that will expand the definition of literacy to include computational skills along with science reasoning. Literacy is an important concept and measurement that has traditionally been used to assess an individual's knowledge of science. This project will explore a broader literacy perspective that incorporates learning derived from out of school and one that incorporates computational skills and thinking as part of a more contemporary perspective of STEM literacy. A prototype web-based App allowing individuals and education organizations to assess literacy levels, and ways to enhance literacy, will be developed and studied. The methodology will be developed using discussions and knowledge from over 60 experts across computing, education, science, social science, and other STEM fields using a Delphi method to engage in reconceptualization of literacy. The hypothesis is that this new STEM+C literacy framework should be structured along four interacting but semi-independent domains: 1) general STEM+C knowledge; 2) self-defined areas of STEM+C knowledge and expertise; 3) attitudes and beliefs related to STEM+C; and 4) the skills and competencies necessary to participate in STEM+C related pursuits and discussions, including measures of modes of STEM+C thinking. Each of these four domains is likely to include numerous sub-domains and associated descriptors, which collectively describe the different aspects of being a STEM+C literate citizen. The application will be designed to provide feedback to individuals on their knowledge, attitudes and skills compared with those of others and suggest ways to enhance and improve their skills and understanding through an embedded feedback mechanism. This project creates public benefit by providing individuals and organizations with a responsive real-time understanding measuring STEM+C literacy, deepening the dialogue about the value of public engagement in science, engineering, technology, math and computing and revealing the dynamic factors that inform STEM+C literacy.
The Science Behind Pixar (SBP) exhibition was the product of a collaborative effort among the Museum of Science, Boston (MOS), Pixar Animation Studios, and the Science Museum Exhibit Collaborative (SMEC). The 13,000 square foot exhibition presented the science, math, and computer science behind Pixar Animation Studios’ animated films and innovation. Before entering SBP, visitors watched a five-minute film that oriented them to the exhibition and discussed its main messages. Visitors then interacted with screen-based and physical interactive exhibits, as well as the technical pipeline of the
This project, an NSF INCLUDES Design and Development Launch Pilot, managed by the University of Nevada, Reno, addresses the grand challenge of increasing underrepresentation regionally in the advanced manufacturing sector. Using the state's Learn and Earn Program Advanced Career Pathway (LEAP) as the foundation, science, technology, engineering and mathematics (STEM) activities will support and prepare Hispanic students for the region's workforce in advanced manufacturing which includes partnerships with Truckee Meadows Community College (TMCC), the state's Governor's Office of Economic Development, Charles River Laboratories, Nevada Established Program to Stimulate Competitive Research (Nevada EPSCoR) and the K-12 community.
The expected outcomes from the project will inform the feasibility, expandability and transferability of the LEAP framework in diversifying the state's workforce locally and the STEM workforce nationally. Formative and summative evaluation will be conducted with a well-matched comparison group. Dissemination of project results will be disseminated through the Association for Public Land-Grant Universities (APLU), STEM conferences and scholarly journals.
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TEAM MEMBERS:
David ShintaniJulie EllsworthKarsten HeiseRobert StachlewitzRegina Tempel
resourceprojectProfessional Development, Conferences, and Networks
Aligning for Impact: Computer Science Pathways Across Contexts [CS-PAC] is an NSF INCLUDES Design and Development Launch Pilot. It broadens participation of students who are underrepresented in computer science by using the convening and policy-making power of the Georgia State Department of Education to coalesce school district leaders to implement K-12 computer science education. The project provides a national model for how to work toward systemic change. With the State Department of Education's coordination, several school districts will collaboratively seek improvements in their own student participation rates. The coordination of data reporting and analysis, resources, communications, and policy promote more equitable participation in computer science education. Research emerging from this project informs other states about how to collaboratively shape computer science education policy and policy implementation.
Using a Collective Impact approach to systemic change, the project creates sustainable institutional change at the community, state, and national levels. Qualitative and quantitative data provide descriptions about how to utilize alignment strategies within Collective Impact in three different contexts: rural, suburban, and urban. Outcomes utilize a regression discontinuity analysis to justify successful implementation as well as qualitative analysis of implementation efforts that were deemed most effective by all stakeholders. The project outputs directly affect over 88,000 students across five districts and indirectly affect over 1.7 million in Georgia alone. The culminating project goal is the development of a coherent framework for aligning K-12 computer science education pathways.
Women continue to be underrepresented in computer science professions. In 2015, while 57% of professional occupations in the U.S. were held by women, only 25% of computing occupations were held by women. Furthermore, the share of computer science degrees going to women is smaller than any STEM field, even though technology careers are the most promising in terms of salaries and future growth. Research suggests that issues contributing to this lack of computer science participation begin early and involve complex social and environmental factors, including girls' perception that they do not belong in computer science classes or careers. Computer science instruction often alienates girls with irrelevant curriculum; non-collaborative pedagogies; a lack of opportunities to take risks or make mistakes; and a heavy reliance on lecture instead of hands-on, project-based learning. Computer science experiences that employ research-based gender equitable best practices, particularly role modeling, can help diminish the gender gap in participation. In response to this challenge, Twin Cities PBS (TPT), the National Girls Collaborative (NGC) and Code.org will lead Code: SciGirls! Media for Engaging Girls in Computing Pathways, a three-year project designed to engage 8-13 year-old girls in coding through transmedia programming which inspires and prepares them for future computer science studies and career paths. The project includes five new PBS SciGirls episodes featuring girls and female coding professionals using coding to solve real problems; a new interactive PBSKids.org game that allows children to develop coding skills; nationwide outreach programming, including professional development for informal educators and female coding professionals to facilitate activities for girls and families in diverse STEM learning environments; a research study that will advance understanding of how the transmedia components build girls' motivation to pursue additional coding experiences; and a third-party summative evaluation.
Code: SciGirls! will foster greater awareness of and engagement in computer science studies and career paths for girls. The PBS SciGirls episodes will feature girls and female computer science professionals using coding to solve real-world challenges. The project's transmedia component will leverage the television content into the online space in which much of 21st century learning takes place. The new interactive PBSKids.org game will use a narrative framework to help children develop coding skills. Drawing on narrative transportation theory and character identification theory, TPT will commission two exploratory knowledge-building studies to investigate: To what extent and how do the narrative formats of the Code: SciGirls! online media affect girls' interest, beliefs, and behavioral intent towards coding and code-related careers? The studies aim to advance understanding of how media builds girls' motivation to pursue computer science experiences, a skill set critical to building tomorrow's workforce. The project team will also raise educators' awareness about the importance of gender equitable computer science instruction, and empower them with best practices to welcome, prepare and retain girls in coding. The Code: SciGirls! Activity Guide will provide educators with a relevant resource for engaging aspiring computer scientists. The new media and guide will also reside on PBSLearningMedia.org, reaching 1.2 million teachers, and will be shared with thousands of educators across the SciGirls CONNECT and National Girls Collaborative networks. The new episodes are anticipated to reach 92% of U.S. TV households via PBS, and the game at PBSKids.org will introduce millions of children to coding. The summative evaluation will examine the reach and impact of the episodes, game and new activities. PIs will share research findings and project resources at national conferences and will submit to relevant publications. This 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 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.
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