Participants in this study reported a variety of resources used in the past to learn to code in Apex, including online tutorials, one-day classes sponsored by Salesforce, and meet-up groups focused on learning. They reported various difficulties in learning through these resources, including what they viewed as the gendered nature of classes where the men already seemed to know how to code—which set a fast pace for the class, difficulty in knowing “where to start” in their learning, and a lack of time to practice learning due to work and family responsibilities. The Coaching and Learning Group
Participants in this study reported a variety of resources used in the past to learn to code in Apex, including online tutorials, one-day classes sponsored by Salesforce, and meet-up groups focused on learning. They reported various difficulties in learning through these resources, including what they viewed as the gendered nature of classes where the men already seemed to know how to code—which set a fast pace for the class, difficulty in knowing “where to start” in their learning, and a lack of time to practice learning due to work and family responsibilities. The Coaching and Learning Group
Grassroots women's learn-to-code groups are springing up in many places. This infographic a study of one such group, in which more-knowledgeable "coaches" lead novice "learners" in learning software programming on the Salesforce platform. This study found that women create such groups to have supportive, non-threatening environments that nuture their learning to build confidence before entering male-dominated software development communities.
This Research Advanced by Interdisciplinary Science and Engineering (RAISE) project is supported by the Division of Research on Learning in the Education and Human Resources Directorate and by the Division of Computing and Communication Foundations in the Computer and Information Science and Engineering Directorate. This interdisciplinary project integrates historical insights from geometric design principles used to craft classical stringed instruments during the Renaissance era with modern insights drawn from computer science principles. The project applies abstract mathematical concepts toward the making and designing of furniture, buildings, paintings, and instruments through a specific example: the making and designing of classical stringed instruments. The research can help instrument makers employ customized software to facilitate a comparison of historical designs that draws on both geometrical proofs and evidence from art history. The project's impacts include the potential to shift in fundamental ways not only how makers think about design and the process of making but also how computer scientists use foundational concepts from programming languages to inform the representation of physical objects. Furthermore, this project develops an alternate teaching method to help students understand mathematics in creative ways and offers specific guidance to current luthiers in areas such as designing the physical structure of a stringed instrument to improve acoustical effect.
The project develops a domain-specific functional programming language based on straight-edge and compass constructions and applies it in three complementary directions. The first direction develops software tools (compilers) to inform the construction of classical stringed instruments based on geometric design principles applied during the Renaissance era. The second direction develops an analytical and computational understanding of the art history of these instruments and explores extensions to other maker domains. The third direction uses this domain-specific language to design an educational software tool. The tool uses a calculative and constructive method to teach Euclidean geometry at the pre-college level and complements the traditional algebraic, proof-based teaching method. The representation of instrument forms by high-level programming abstractions also facilitates their manufacture, with particular focus on the arching of the front and back carved plates --- of considerable acoustic significance --- through the use of computer numerically controlled (CNC) methods. The project's novelties include the domain-specific language itself, which is a programmable form of synthetic geometry, largely without numbers; its application within the contemporary process of violin making and in other maker domains; its use as a foundation for a computational art history, providing analytical insights into the evolution of classical stringed instrument design and its related material culture; and as a constructional, computational approach to teaching geometry.
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.
African American and Latinx youth are often socialized towards athletic activity and sports participation, sometimes at the expense of their exploration of the range of potential career paths including those in the science, technology, engineering, and mathematics (STEM) fields. This project will immerse middle school youth in the rapidly growing world of sports data analytics and build their knowledge of statistics concepts and the data science process. The project will focus on the STEM interests and knowledge development of African American and Latinx youth, an underrepresented and underserved group in STEM. Researchers will explore the ways youths' social identities can and should serve as bridges towards future productive academic and professional identities including those associated with STEM learning and the STEM professions. The outcomes of the project will advance knowledge in promoting elements of informal learning experiences that build adolescents' motivation and persistence for productive participation in STEM courses and careers. This project is funded by the Advancing Informal STEM Learning program (AISL), which seeks to advance new approaches to and evidence-based understanding of the design and development of STEM learning opportunities for the public in informal environments, and the Innovative Technology Experiences for Students and Teachers program (ITEST), which funds projects that leverage innovative uses of technologies to prepare diverse youth for the STEM workforce, with a focus on broadening participation among underrepresented and underserved groups in STEM fields.
Over a three-year period, 250 middle school learners in the West Baltimore, Maryland and Hyattsville, Maryland areas will engage in three main learning activities: Summer Camp (three weeks), Sports Day Saturdays, and a Spring Summit. Through a partnership between the University of Maryland and Coppin State University, the project will utilize resources in multiple departments and units across both universities, and engage with youth sports leagues such as the American Athletic Union (AAU) to support participants' engagement in the data science process including collection of raw data, exploration of data, development of models, visualization, communication, and reporting of data, and data-driven decision making. Furthermore, youth participants will attend local AAU, college, and professional sporting events, and interact with members of coaching staffs to better understand the ways performance data technologies are utilized to inform recruitment and team performance. The mixed-methods research agenda for this project is guided by three main questions: (1) What elements of the project's model are most successful at supporting congruence of adolescents' academic identity, including STEM identity and social identity including athletic identity? (2) What elements support adolescents' motivation, and persistence for productive participation in current and future STEM courses? (3) To what extent did the project appear to influence participants' perceptions of their future professions? At multiple points throughout the experience, participants will complete surveys designed to document and assess statistics and data science knowledge; interest in STEM careers; academic, social and athletic identity development; and STEM course taking patterns. Researchers will also observe project activities, interview a focal group of participants, and survey participants' parents to identify elements of learning experiences that encourage and support adolescents' interest in STEM disciplines and STEM professions. The project team will develop conceptual and pedagogical frameworks that support middle school youth' engagement and interest in science, engineering, technology, and mathematics through repurposing spaces where these youths frequent. A major outcome of the project will be workforce preparation and offers a promising approach for encouraging youth to persist along STEM pathways, which may ultimately result in broadened participation in STEM workforces.
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.
Despite the ubiquity of Artificial Intelligence (AI), public understanding of how it works and is used is limited This project will research, design, and develop innovative approaches focusing on Artificial Intelligence (AI) for under-represented youth ages 14-24. Program components include live social media chats with AI leaders, app development, journalistic investigations of ethical issues in machine learning, and review of AI-based consumer products. Youth Radio is a non-profit media and tech organizations that provides youth with skills in STEM, journalism, arts, and communications. They engage 250 youth annually through free after-school classes and work shifts. Participants are 90% youth of color and 80% low income. Project partners include the MIT Media Lab which developed App Inventor which allows novice users to build fully functional apps. Staff from Google will serve as a project advisor on the curriculum. The project has exceptional national reach through the dissemination of its media and apps through national outlets such as NPR and Teen Vogue as well as various platforms including online, on-air, as well as presentations, publications, and training tools. The project broadens participation by engaging these low income youth of color in developing skills critical to the workforce of the future. It will help prepare an upcoming generation of Artificial Intelligence creators, users, and consumers who understand the technology and embrace and encourage its potential.It will give them the necessary knowledge and opportunities for careers in an AI-driven future.
This project is grounded in sociocultural learning theory and practice and is interdisciplinary by design. The theoretical framework holds that Computational Thinking plus Critical Pedagogy leads to Critical Computational Literacy. Also, Digital Age Civics plus Participatory Culture leads to Civic Imagination helping youth build a better world through technology. The driving research questions include: What do underrepresented youth understand about AI and its role in society? What are the ethical dilemmas posed by AI from their vantage point? What are the features of an engaging ethics-centered pedagogy with AI? What impact do the AI products developed by the youth have on the target audience? The research design will use ethnographic techniques and design research to study and analyze youth learning. Data sources will include baseline surveys, audio recordings and transcriptions from learning sessions with the participants, research analytic memos, focus group interviews, student-generating artifacts of learning and finished products, etc. The design-based approach will enable systematic, evidence-based iteration on the initiative's activities, pedagogical approach and products. An independent summative evaluation will provide complementary data and perspective to triangulate with the research findings.
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.
DATE:
-
TEAM MEMBERS:
Elisabeth SoepEllin O'LearyHarold Abelson
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.
DATE:
-
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.
DATE:
-
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.
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
The Computational Thinking in Ecosystems (CT-E) project is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. The project is a collaboration between the New York Hall of Science (NYSCI), Columbia University's Center for International Earth Science Information Network, and Design I/O. It will address the need for improved data, modeling and computational literacy in young people through development and testing of a portable, computer-based simulation of interactions that occur within ecosystems and between coupled natural and human systems; computational thinking skills are required to advance farther in the simulation. On a tablet computer at NYSCI, each participant will receive a set of virtual "cards" that require them to enter a computer command, routine or algorithm to control the behavior of animals within a simulated ecosystem. As participants explore the animals' simulated habitat, they will learn increasingly more complex strategies needed for the animal's survival, will use similar computational ideas and skills that ecologists use to model complex, dynamic ecological systems, and will respond to the effects of the ecosystem changes that they and other participants elicit through interaction with the simulated environment. Research on this approach to understanding interactions among species within biological systems through integration of computing has potential to advance knowledge. Researchers will study how simulations that are similar to popular collectable card game formats can improve computational thinking and better prepare STEM learners to take an interest in, and advance knowledge in, the field of environmental science as their academic and career aspirations evolve. The project will also design and develop a practical approach to programing complex models, and develop skills in communities of young people to exercise agency in learning about modeling and acting within complex systems; deepening learning in young people about how to work toward sustainable solutions, solve complex engineering problems and be better prepared to address the challenges of a complex, global society.
Computational Thinking in the Ecosystems (CT-E) will use a design-based study to prototype and test this novel, tablet-based collectable card game-like intervention to develop innovative practices in middle school science. Through this approach, some of the most significant challenges to teaching practice in the Next Generation Science Standards will be addressed, through infusing computational thinking into life science learning. CT-E will develop a tablet-based simulation representing six dynamic, interconnected ecosystems in which students control the behaviors of creatures to intervene in habitats to accomplish goals and respond to changes in the health of their habitat and the ecosystems of which they are a part. Behaviors of creatures in the simulation are controlled through the virtual collectable "cards", with each representing a computational process (such as sequences, loops, variables, conditionals and events). Gameplay involves individual players choosing a creature and habitat, formulating strategies and programming that creature with tactics in that habitat (such as finding food, digging in the ground, diverting water, or removing or planting vegetation) to navigate that habitat and survive. Habitats chosen by the participant are part of particular kinds of biomes (such as desert, rain forest, marshlands and plains) that have their own characteristic flora, fauna, and climate. Because the environments represent complex dynamic interconnected environmental models, participants are challenged to explore how these models work, and test hypotheses about how the environment will respond to their creature's interventions; but also to the creatures of other players, since multiple participants can collaborate or compete similar to commercially available collectable card games (e.g., Magic and Yu-Go-Oh!). NYSCI will conduct participatory design based research to determine impacts on structured and unstructured learning settings and whether it overcomes barriers to learning complex environmental science.
AHA! Island is a new project that uses animation, live-action videos, and hands-on activities to support joint engagement of children and caregivers around computational thinking concepts and practices. This research is intended to examine the extent to which the prototyped media and activity sets support the project’s learning goals. Education Development Center (EDC), WGBH’s research partner for the project, conducted a small formative study with 16 English-speaking families (children and their caregivers) to test out these media and activity set prototypes. During the in-person video