Computing fields are foundational to most STEM disciplines and the only STEM discipline to show a consistent decline in women's representation since 1990, making it an important field for STEM educators to study. The explanation for the underrepresentation of women and girls in computing is twofold: a sense that they do not fit within the stereotypes associated with computing and a lack of access to computer games and technologies beginning at an early age (Richard, 2016).
Informal coding education programs are uniquely situated to counter these hurdles because they can offer additional
Overlaying Computer Science (CS) courses on top of inequitable schooling systems will not move us toward “CS for All.” This paper prioritizes the perspectives of minoritized students enrolled in high school CS classrooms across a large, urban school district in the Western United States, to help inform how CS can truly be for all.
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TEAM MEMBERS:
Jean RyooTiera TanksleyCynthia EstradaJane Margolis
Virtual Reality (VR) shows promise to broaden participation in STEM by engaging learners in authentic but otherwise inaccessible learning experiences. The immersion in authentic learner environments, along with social presence and learner agency, that is enabled by VR helps form memorable learning experiences. VR is emerging as a promising tool for children with autism. While there is wide variation in the way people with autism present, one common set of needs associated with autism that can be addressed with VR is sensory processing. This project will research and model how VR can be used to minimize barriers for learners with autism, while also incorporating complementary universal designs for learning (UDL) principles to promote broad participation in STEM learning. As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches, and resources for use in a variety of settings. This project will build on a prototype VR simulation, Mission to Europa Prime, that transports learners to a space station for exploration on Jupiter's moon Europa, a strong candidate for future discovery of extraterrestrial life and a location no human can currently experience in person. The prototype simulation will be expanded to create a full, immersive STEM-based experience that will enable learners who often encounter cognitive, social, and emotional barriers to STEM learning in public spaces, particularly learners with autism, to fully engage and benefit from this STEM-learning experience. The simulation will include a variety of STEM-learning puzzles, addressing science, mathematics, engineering, and computational thinking through authentic and interesting problem-solving tasks. The project team's learning designers and researchers will co-design puzzles and user interfaces with students at a post-secondary institute for learners with autism and other learning differences. The full VR STEM-learning simulation will be broadly disseminated to museums and other informal education programs, and distributed to other communities.
Project research is designed to advance knowledge about VR-based informal STEM learning and the affordances of VR to support learners with autism. To broaden STEM participation for all, the project brings together research at the intersection of STEM learning, cognitive and educational neuroscience, and the human-technology frontier. The simulation will be designed to provide agency for learners to adjust a STEM-learning VR experience for their unique sensory processing, attention, and social anxiety needs. The project will use a participatory design process will ensure the VR experience is designed to reduce barriers that currently exclude learners with autism and related conditions from many informal learning opportunities, broadening participation in informal STEM learning. Design research, usability, and efficacy studies will be conducted with teens and adults at the Pacific Science Center and Boston Museum of Science, which serve audiences with autism, along with the general public. Project research is grounded in prior NSF-funded research and leverages the team's expertise in STEM learning simulations, VR development, cognitive psychology, universal design, and informal science education, as well as the vital expertise of the end-user target audience, learners with autism. In addition to being shared at conferences, the research findings will be submitted for publication to peer-reviewed journals for researchers and to appropriate publications for VR developers and disseminators, museum programs, neurodiverse communities and other potentially interested parties.
This Innovations in Development 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.
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TEAM MEMBERS:
Teon EdwardsJodi Asbell-ClarkeJamie LarsenIbrahim Dahlstrom-Hakki
Sense-making with data through the process of visualization—recognizing and constructing meaning with these data—has been of interest to learning researchers for many years. Results of a variety of data visualization projects in museums and science centers suggest that visitors have a rudimentary understanding of and ability to interpret the data that appear in even simple data visualizations. This project supports the need for data visualization experiences to be appealing, accommodate short and long-term exploration, and address a range of visitors’ prior knowledge. Front-end evaluation
The data collection procedure and process is one of the most critical components in a research study that affects the findings. Problems in data collection may directly influence the findings, and consequently, may lead to questionable inferences. Despite the challenges in data collection, this study provides insights for STEM education researchers and practitioners on effective data collection, in order to ensure that the data is useful for answering questions posed by research. Our engineering education research study was a part of a three-year, NSF funded project implemented in the Midwest
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TEAM MEMBERS:
Ibrahim YeterAnastasia Marie RynearsonHoda EhsanAnnwesa DasguptaBarbara FagundesMuhsin MeneskeMonica Cardella
We found that the learners seeking out resources to teach themselves to code were generally college educated women who were motived either by the desire to be able to read and understand the code written by hired developers or the desire to become developers themselves. The importance of a female-focused learning setting was mixed; while most women acknowledged a more comfortable atmosphere created by such a setting, very few cited that as a primary reason for joining the group.
All learner participants in this study persisted through the ten weeks of the Women’s Coaching and Learning
In 2016, ETR received a National Science Foundation grant to study, under Principal Investigator Louise Ann (“Lou Ann”) Lyon, PhD, a newly formed, real-world organization dedicated to helping women in the workforce learn to write computer code. This project formed a partnership between a research team with experience in computer science (CS) education and learning sciences research and a newly fashioned practitioner team focused on building a grassroots, informal, volunteer group created to help women help themselves and others learn to write computer code. This research-practitioner
Increasingly, scientists and their institutions are engaging with lay audiences via media. The emergence of social media has allowed scientists to engage with publics in novel ways. Social networking sites have fundamentally changed the modern media environment and, subsequently, media consumption habits. When asked where they primarily go to learn more about scientific issues, more than half of Americans point to the Internet. These online spaces offer many opportunities for scientists to play active roles in communicating and engaging directly with various publics. Additionally, the proposed research activities were inspired by a recent report by the National Academies of Sciences, Engineering, and Medicine that included a challenge to science communication researchers to determine better approaches for communicating science through social media platforms. Humor has been recommended as a method that scientists could use in communicating with publics; however, there is little empirical evidence that its use is effective. The researchers will explore the effectiveness of using humor for communicating about artificial intelligence, climate science and microbiomes.
The research questions are: How do lay audiences respond to messages about scientific issues on social media that use humor? What are scientists' views toward using humor in constructing social media messages? Can collaborations between science communication scholars and practitioners facilitate more effective practices? The research is grounded in the theory of planned behavior and framing as a theory of media effects. A public survey will collect and analyze data on Twitter messages with and without humor, the number of likes and re-tweets of each message, and their scientific content. Survey participants will be randomly assigned to one of twenty-four experimental conditions. The survey sample, matching recent U.S. Census Bureau data, will be obtained from opt-in panels provided by Qualtrics, an online market research company. The second component of the research will quantify the attitudes of scientists toward using humor to communicate with publics on social media. Data will be collected from a random sample of scientists and graduate students at R1 universities nationwide. Data will be analyzed using descriptive statistics and regression modeling.
The broader impacts of this project are twofold: findings from the research will be shared with science communication scholars and trainers advancing knowledge and practice; and an infographic (visual representation of findings) will be distributed to practitioners who participate in research-practice partnerships. It will provide a set of easily-referenced, evidence-based guidelines about the types of humor to which audiences respond positively on social media.
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:
Sara YeoLeona Yi-Fan SuMichael Cacciatore
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
Robots and robotics excite and challenge youths and adults. Unfortunately, the cost of purchasing robots or building useful robots is prohibitive for many low resource individuals and groups. This project will relieve this expense and provide an opportunity for resource limited individuals to experience the thrilling aspects of robotics by building a computer game that simulates robotic action. This project uses co-robotics wherein the participating player programs an avatar to assist in a symbiotic manner to achieve the goals of the game and participant. The game will provide access to the ideas and concepts such as programing, computational thinking and role assumption. The overarching goals are (1) to engage low-resource learners in STEM education through robotics in out-of-school spaces, and (2) to update the field of robotics-base STEM education to integrate the co-robotics paradigm.
This project is designed to gain knowledge on how co-robotics can be used in the informal education sector to facilitate the integration of computational science with STEM topics and to expand the educational use of co-robotics. Because the concept of co-robotics is new, a designed-based research approach will be used to build theoretical knowledge and knowledge of effective interventions for helping participants learn programing and computational thinking. Data will be collected from several sources including surveys, self-reports, in game surveys, pre and post-tests. These data collection efforts will address the following areas: Technology reliability, Resolution of cognitive tension around co-play, Accelerate discovery and initial engagement, Foster role-taking and interdependence with co-robots, Investigate social learning, and Validate measures using item response theory analysis. The DBR study questions are:
1.What design principles support the development of P3Gs that can effectively attract initial engagement in a free-choice OST space that offers large numbers of competing options? 2.What design principles support a P3G gameplay loop that enables learning of complex skills, computational thinking and co-robotics norms, and building of individual and career interest over the course of repeated engagement?
3.What design principles support P3Gs in attaining a high rate of re-engagement within low-resource OST settings? 4.What kinds of positive impact can P3Gs have on their proximal and distal environment? In addition, the project will research these questions about design: 1.What technical and game design features are needed to accommodate technological interruption? 2.What design elements or principles mitigate competition for cognitive resources between real-time play and understanding the co-robotic's behavior in relation to the code the player wrote for it? 3.What design elements are effective at getting learners in OST settings to notice and start playing the game? 4.What designs are effective at encouraging learners to engage with challenging content, particularly the transition from manual play to co-play? 5.What design elements help players develop a stake in the role the game offers? 6.What social behaviors emerge organically around a P3G prototype that is not designed to evoke specific social interactions?
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.
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. This AISL project investigates how informal programs can broaden participation by building social capital in STEM for youth from underrepresented groups. The project integrates social network analysis with research on informal learning, and draws on a framework to connect learning across a variety of sectors. It builds on evidence that sponsorship of youth interest, affinity-based mentorship, and brokering connections to other settings and opportunities can build social capital and support interest and persistence in STEM. It represents a strategic and timely investment into research that solidifies these emerging insights from research and practice, conducting focused investigation into relational supports for STEM interests that are particularly well suited to informal programs.
The project is guided by two research questions: (1) What forms of social capital are tied to persistence in and connecting across informal STEM programs for youth from underrepresented groups? (2) What program features--specifically sponsorship, mentorship, and brokering--grow these social supports for persistence in and connecting across informal STEM programs for underserved youth? These questions are addressed through a mixed methods 18-month cross-sectional study of 200 students in three informal programs in Orange County, California that offer project-based engineering and coding programs, support mentorship, and focus on groups underrepresented in STEM. The sample will include three age categories, capturing the transition to high school, persistence during high school, and transition to college and career. Teens will be interviewed three times at 6-month intervals, spanning these transitions. The goal of this research and effort is to determine if social capital plays an extra ordinary role in learning by this group.
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.
Cities and communities in the U.S. and around the world are entering a new era of transformational change, in which their inhabitants and the surrounding built and natural environments are increasingly connected by smart technologies, leading to new opportunities for innovation, improved services, and enhanced quality of life. The Smart and Connected Communities (SCC) program supports strongly interdisciplinary, integrative research and research capacity-building activities that will improve understanding of smart and connected communities and lead to discoveries that enable sustainable change to enhance community functioning. This project is a Research Coordination Network (RCN) that focuses on achieving SCC for medium/small size, remote, and rural communities through a polycentric (multiple centers) integrated policy, design, and technology approach. The communities served by the RCN have higher barriers to information, resources, and services than larger urban communities. To reduce this gap, the PIs propose to develop need-based R&D pipelines to select solutions with the highest potential impacts to the communities. Instead of trying to connect under-connected communities to nearby large cities, this proposal aims to develop economic opportunities within the communities themselves. This topic aligns well with the vision of the SCC program, and the proposed RCN consists of a diverse group of researchers, communities, industry, government, and non-profit partners.
This award will support the development of an RCN within the Commonwealth of Virginia which will coordinate multiple partners in developing innovations utilizing smart and connected technologies. The goal of the research coordination network is to enable researchers and citizens to collaborate on research supporting enhanced quality of life for medium, small, and rural communities which frequently lack the communication and other infrastructure available in cities. The research coordination network will be led by the University of Virginia. There are 14 partner organizations including six research center partners in transportation, environment, architecture and urban planning, and engineering and technology; two State and Industry partners (Virginia Municipal League and Virginia Center for Innovative Technology); four community partners representing health services (UVA Center for Telemedicine), small and remote communities (Weldon Cooper Center), neighborhood communities (Charlottesville Neighborhood Development), and urban communities (Thriving Cities); and two national partners which support high speed networking (US-Ignite) and city-university hubs (MetroLab). Examples of research coordination include telemedicine services, transportation services, and user-centric and community-centric utilization and deployment of sensor technologies.
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TEAM MEMBERS:
Ila BermanT. Donna ChenKaren RheubanQian Cai