Making experiences and activities are rich with opportunities for mathematical reasoning that often go unrecognized by both participants and educators. Since 2015, we have been exploring this potential through the Math in the Making initiative. The work focuses particularly on children’s museums and science centers, many of which have developed maker spaces and programs over the last decade. In this article, we share insights from our most recent round of research. To begin, we consider the fundamental question of what it means to authentically integrate mathematics with making.
This AISL Pilots and Feasibility project will study the data science learning that takes place as members of the public explore and analyze open civic data related to their everyday lives. Government services, such as education, transportation, and non-emergency municipal requests, are becoming increasingly digital. Generally, program workshops and events may be able to support participants in using such data to answer their own questions, such as: "How do City agencies respond to noise in my neighborhood?" and "How do waste and recycling services in my neighborhood compare with others?" This project seeks to understanding how such programs are designed and facilitated to support diverse communities in accessing and meaningfully analyzing data will promote innovation and knowledge building in informal data science education. The team will begin by summarizing best practices in data science education from a variety of fields. Next they will explore the design and impacts of two programs in New York City, a leader in publicly available Open Data initiatives. This phase will explore activities and facilitation approaches, participants' objectives and data literacy skills practice, and begin to identify potential barriers to entry and levels of participation. Finally, the team will build capacity for other similar organizations to explore and understand their impacts on community members' engagement with civic data. This pilot study will establish preliminary evidence of the effectiveness of these programs, and in turn, inform future research into the identifying and amplifying best practices to support public engagement with data.
This research team will begin by synthesizing data science learning best practices based on varied literatures and surveys with academic and practitioner experts.
Synthesis results will be applied as a lens to gather preliminary evidence regarding the impacts of two programs on participants' data science practices and understanding of the nature of data in the context of civics. The programs include one offered by the Mayor's Office of Data Analytics (MODA), which is the NYC agency with overall responsibility for the City's Open Data programs, and BetaNYC, a leading nonprofit organization working to improve lives through civic design, technology, and engagement with government open data. The research design triangulates ethnographic observations and artifacts, pre and post adapted surveys, and interviews with participants and facilitators. Researchers will identify programmatic metrics and adapts existing measures to assess various outcomes related to public engagement with data, including: question formulation, data set selection and manipulation, the use of data to make inferences, and understanding variability, sampling and context. These metrics will be shared through an initial assessment framework for data science learning in the context of community engagement with civic open data. Researchers will also begin to identify barriers to broader participation through literature synthesis, interviews with participants and facilitators, and conversations with other organizations in our networks, such as NYC Community Boards. Findings will determine the suitability of the programs under study and inform future research to identify and amplify best practices in supporting public engagement with data.
This project is funded by the NSF Advancing Informal STEM Learning 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.
This Pilots and Feasibility Studies 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.
Research shows that algebra is a major barrier to student success, enthusiasm and participation in STEM for under-represented students, particularly African-American students in under-resourced high schools. Programs that develop ways to help students master algebra concepts and a belief that they can perform algebra may lead to more students entering engineering careers. This project will provide an online engineering program to support 9th and 10th grade Baltimore City Public Schools students, a predominantly low-income African-American cohort, to develop concrete goals of becoming engineers. The goals of the program are to help students with a growing interest in engineering to maintain that interest throughout high school. The project will also support students aspire to an engineering career. The project will develop in students an appreciation of requisite courses and skills, and increase self-efficacy in mathematics. The project will also develop a replicable model of informal education capable of reinforcing the mathematical foundations that students learn during the school day. Additionally, the project will broaden participation in engineering by being available to students during out-of-school time and by having relaxed entrance criteria compared to existing opportunities in supplemental engineering curricula. The project is a collaboration between the Baltimore City Public Schools, Johns Hopkins University Applied Physics Laboratory, Northrop Grumman Corporation, and Expanded School-Based Mental Health programs to support students both during and after participation. The project will benefit society by providing skills that will allow high school students to become members of tomorrow's highly trained STEM workforce.
The research will test whether an informal, scaffolded online algebra-for-engineering program increases students' mastery and self-efficacy in mathematics. The research will advance knowledge regarding informal education by applying Social Cognitive Career Theory as a framework for measuring program impact. The theoretical framework will aid in identifying mechanisms through which students with interest in engineering might persist in maintaining this interest through high school via algebra skill mastery and increased self-efficacy. The project will recruit 200 youth from the Baltimore City Public Schools to participate in the project over three years. Qualitative data will be collected to assess how student and school socioeconomic factors impact implementation, student engagement, and outcomes. The research will answer the following questions: 1) What effect does program participation have on math mastery? 2) What direct and indirect effects do program completion and supports have on students' mathematics self-efficacy? 3) What direct and indirect effects do program components have on engineering career goals by the end of the program? 4) What direct and indirect effects does math self-efficacy have on career goals? 5) To what extent are the effects of program participation on engineering career goals mediated by math self-efficacy and engineering interest? 6) How do school factors relate to the implementation of the program? 7) What socioeconomic-related factors relate to the regularity and continuation of student participation in the program? The quantitative methods of data analysis will employ descriptive and multivariate statistical methods. Qualitative data from interviews will be analyzed using an emergent approach and a coding scheme guided by theoretical constructs. Project results will be communicated to scholars and practitioners. The team will also share information through school newsletters and parent communication through Baltimore City Public Schools.
This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.
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.
As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public's engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner's speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants. The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry's (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy - currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.
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 Research in Service to Practice project will address the issues around Informal Education of rural middle school students who have high potential regarding academic success in efforts to promote computer and IT knowledge, advanced quantitative knowledge, and STEM skills. Ten school districts in rural Iowa will be chosen for this study. It is anticipated that new knowledge on rural informal education will be generated to benefit the Nation's workforce. The specific objectives are to understand how informal STEM learning shapes the academic and psychosocial outcomes of rural, high-potential students, and to identify key characteristics of successful informal STEM learning environments for rural, high-potential students and their teachers. The results of this project will provide new tools for educators to increase the flow of underserved students into STEM from economically-disadvantaged rural settings.
The President's Council of Advisors on Science and Technology predicts a rapid rise in the number of STEM jobs available in the next decade, describing an urgent need for students' educational opportunities to prepare them for this workforce. In 2014, 62% of CEOs of major US corporations reported challenges filling positions requiring advanced computer and information technology knowledge. The project team will use a mixed methods approach, integrating comparative case study and mixed effects longitudinal methods, to study the Excellence program. Data sources include teacher interviews, classroom observations, and student assessments of academic aptitude and psychosocial outcomes. The analysis and evaluation of the program will be grounded in understanding the local efforts of school districts to build curriculum responsive to the demands of their high-potential student body. The project design, and subsequent analysis plan, utilizes a mixed methods approach, incorporating case study and longitudinal quantitative methods to analyze naturalistic data and build robust evidence for the implementation and impact of this program. This project will provide significant insights in how best to design, implement, and support informal out-of-school learning environments to broaden participation in the highest levels of STEM education and careers for under-resourced rural students.