This NSF INCLUDES Design and Development Launch Pilot will improve math achievement among elementary school students of color in public schools in Albuquerque, New Mexico. Recognizing the need to coordinate efforts related to students' math and science achievement, key stakeholders formed the NM STEM Ecosystem, a dynamic network of cross-sector partners committed to making real impact on STEM education and degree attainment in Albuquerque. The NM STEM Ecosystem identified the math achievement gap between low-income students of color and their more economically-advantaged peers as the Broadening Participation (BP) Challenge it would address first. While math achievement gaps between students of color and Caucasian students appear nationally, the situation is particularly dire in New Mexico. In order to keep doors open to future STEM careers, it is crucial that learning pathways for math are articulated early and that these pathways honor families' cultural ways of knowing. The innovative strategy of Math Families & Communities Empowering Student Success (Math FACESS) is to use a collective impact approach to close the math achievement gap by connecting formal and informal STEM educators around a coherent, multi-faceted program of early mathematics teaching and learning that empowers parents and teachers to support children's mathematical development. Implementation of Math FACESS includes four major components: 1) Teachers at two pilot schools will participate in professional development related to Math Talk and Listening; 2) Parents at the pilot schools will participate in parent workshops and community-based activities focused on supporting their children's math achievement; 3) Project partners will implement community-based family activities organized around a theme of Twelve Months of Math; and 4) Ecosystem partners will study what worked and what didn't, in order to identify best practices that can be shared with system leaders to scale effective practices and increase impact.
The near-term objectives for Math FACESS are: 1) improve students' attitudes, practices, and achievement in math; 2) improve parents' attitudes, practices, and confidence in math and increase their utilization of family math resources; 3) improve data-sharing among partners related to math participation and achievement; and 4) create pathways within the Ecosystem for family math learning. The effectiveness of the collective impact model and impacts on partner organizations also will be assessed. Through the math FACESS Launch Pilot, the NM STEM Ecosystem plans to: 1) demonstrate the power of a collective impact social innovation framework to address a systemic community condition -- in this case, the math achievement gap; 2) contribute to theory-of-change research that demonstrates student achievement can be affected by working with parents and teachers; and 3) provide a model that values different ways of knowing and uses cultural context in the design of STEM learning opportunities for students, families, and schools.
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TEAM MEMBERS:
Joe HastingsArmelle CasauObenshain KorenKersti TysonAngelo Gonzales
The University of Texas at Austin's Texas Advanced Computing Center, Chaminade University of Honolulu (CUH), and the Georgia Institute of Technology will lead this NSF INCLUDES Design and Development Launch Pilot (DDLP) to establish a model for data science preparation of Native Hawaiian and Pacific Islander (NHPI) students at the high school and undergraduate levels. The project is premised on the promise of NHPI communities gaining access to, and the ability to work with, large data sets to tackle emerging problems in the Pacific. Such agency over "big data" sets that are relevant to Pacific issues, and contemporary skills in data science, analytics and visualization have the potential to be transformative for community improvement efforts. The effort has the potential to advance knowledge, instructional pedagogy and practices to improve NHPI high school and undergraduate students performance in and attraction to STEM education and careers.
The project team will work to: 1) Increase interest and proficiency in data science and visualization among NHPI high school and undergraduate students through a summer immersion experience that bridges computation and culture; 2) Build data science capacity at an NHPI serving undergraduate institution (CUH) through creation of a certificate program; and 3) Develop and expand partnerships with other organizations with related goals working with NHPI populations. The month-long summer training for 20 NHPI college students, and five NHPI high school students, takes place at CUH and focuses on data science, visualization, and virtual reality, including working on problem sets that require data science approaches and incorporate geographically, socially- and culturally-relevant research themes.
Improving retention rates in postsecondary engineering degree programs is the single most effective approach for addressing the national shortage of skilled engineers. Both mathematics course placement and performance are strong graduation predictors in engineering, even after controlling for demographic characteristics. Underrepresented students (e.g., rural students, low-income students, first-generation students, and students of color) are disproportionately represented in cohorts that enter engineering programs not yet calculus-ready. Frequently, the time and cost of obtaining an engineering degree is increased, and the likelihood of obtaining the degree is also reduced. This educational problem is particularly acute for African American students who attended select high schools in South Carolina, with extremely high-poverty rates. As a result, the investigators proposed an NSF INCLUDES Launch Pilot project to develop a statewide consortium in South Carolina - comprising all of the public four-year institutions with ABET-approved engineering degree programs, all of the technical colleges, and 118 high schools with 70% or higher poverty rates, to pinpoint and address the barriers that prevent these students from being calculus ready in engineering.
This NSF INCLUDES Launch Pilot project will map completion/attrition pathways of students by collecting robust cross-sectional data to identify and understand the complex linkages between and behind critical decisions. Such data have not been available to this extent, especially focused on diverse populations. Further, by developing structural equation models (SEMs), the investigators will be able to build on extant research, contributing directly to understanding the relative impact of a range of latent variables on the development of engineering identity, particularly among African American, rural, low-income, and first-generation engineering students. Results of the pilot interventions are likely to contribute to the empirical and theoretical literature that focus on engineering persistence among underrepresented populations. Project plans also include developing a centralized database compatible to the Multiple Institution Database for Investigation of Engineering Longitudinal Development (MIDFIELD) project to share institutional data with K-12 and postsecondary administrators, engineering educators, and education researchers with NSF INCLUDES projects and beyond.
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TEAM MEMBERS:
Anand GramopadhyeDerek BrownEliza GallagherKristin Frady
In this NSF INCLUDES Design and Development Launch Pilot the institutions of "Building on Strengths" propose to build and pilot the infrastructure, induction process, and early implementation of the Mathematician Affiliates of Color network. This network will consist of mathematicians of color from across academia and industry who want to invest time in, share their expertise with, and learn from students of color and their teachers. Building on Strengths will draw on basic needs cognitive theory to support these interactions and will focus narrowly on short and moderate term collaborations (from one month to a semester) between visiting mathematicians, students, and collaborating teachers that will involve three specific types of interactions: doing mathematics together as a habits-of-mind practice, talking about the discipline of mathematics and the experiences of mathematicians of color in that discipline, and relationship-building activities. The foundational infrastructure developed in the project will include systems for recruitment, selection and induction, a process for pairing affiliate mathematicians with classrooms, and support structures for the collaborations. To support the goals of the network a prototype virtual space will be developed in which real-time artifacts can be collected and shared from the classroom interactions. While Building on Strengths will pilot this program in the secondary context, once a viable model is established, scaling to K-16, as well as to other STEM fields, will be possible.
The research study in the project uses an exploratory sequential mixed-methods design and will be conducted in two phases. In the first, quantitative, phase of the study the following questions will be addressed: (1) Is the teacher-mathematician collaboration associated with a change for students in perception of basic human needs being met, mathematical or racial identities, or beliefs about mathematics or who can do mathematics? (2) Is the teacher-mathematician collaboration associated with a change for adults in perceptions of the role of basic needs or in adults' identities or beliefs about mathematics or who can do mathematics? In the second, qualitative, phase of the study, two types of interactions will be selected for in-depth qualitative study, identifying cases where groups of students experienced changes in their needs, identity, and beliefs. In this qualitative case-centered phase, the following questions will be explored: (1) What is the nature of the mentor-student interaction? (2) What aspects of the intervention do students feel are most relevant to them? (3) How did the implementation of the intervention differ from the anticipated intervention? The results of the study will help improve the infrastructure for, and better support the interactions between, mathematicians of color, students of color and their mathematics teachers; the outcomes will also shed light on how students experience their interactions.
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TEAM MEMBERS:
Michael YoungMaisha MosesAlbert CuocoEden Badertscher
The Sustainability Teams Empower and Amplify Membership in STEM (S-TEAMS), an NSF INCLUDES Design and Development Launch Pilot project, will tackle the problem of persistent underrepresentation by low-income, minority, and women students in STEM disciplines and careers through transdisciplinary teamwork. As science is increasingly done in teams, collaborations bring diversity to research. Diverse interactions can support critical thinking, problem-solving, and is a priority among STEM disciplines. By exploring a set of individual contributors that can be effect change through collective impact, this project will explore alternative approaches to broadly enhance diversity in STEM, such as sense of community and perceived program benefit. The S-TEAMS project relies on the use of sustainability as the organizing frame for the deployment of learning communities (teams) that engage deeply with active learning. Studies on the issue of underrepresentation often cite a feeling of isolation and lack of academically supportive networks with other students like themselves as major reasons for a disinclination to pursue education and careers in STEM, even as the numbers of underrepresented groups are increasing in colleges and universities across the country. The growth of sustainability science provides an excellent opportunity to include students from underrepresented groups in supportive teams working together on problems that require expertise in multiple disciplines. Participating students will develop professional skills and strengthen STEM- and sustainability-specific skills through real-world experience in problem solving and team science. Ultimately this project is expected to help increase the number of qualified professionals in the field of sustainability and the number of minorities in the STEM professions.
While there is certainly a clear need to improve engagement and retention of underrepresented groups across the entire spectrum of STEM education - from K-12 through graduate education, and on through career choices - the explicit focus here is on the undergraduate piece of this critical issue. This approach to teamwork makes STEM socialization integral to the active learning process. Five-member transdisciplinary teams, from disciplines such as biology, chemistry, computer and information sciences, geography, geology, mathematics, physics, and sustainability science, will work together for ten weeks in summer 2018 on real-world projects with corporations, government organizations, and nongovernment organizations. Sustainability teams with low participation by underrepresented groups will be compared to those with high representation to gather insights regarding individual and collective engagement, productivity, and ongoing interest in STEM. Such insights will be used to scale up the effort through partnership with New Jersey Higher Education Partnership for Sustainability (NJHEPS).
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TEAM MEMBERS:
Amy TuiningaAshwani VasishthPankaj Lai
There is a growing need for citizens to be able to work with data and consider how data is represented. This work employs a design, make, play framework to create data modeling learning experiences for young children and their caregivers in an informal setting. The project develops and tests a curriculum for a workshop series for 5-8 year old children to engage them in playful exploration of data modeling. Children engage in data collection, data representation, and data analysis by drawing on their own experiences of museum exhibitions. The curriculum supports developing children's interest and engagement with data science and data literacy, which are foundational knowledge for a range of STEM careers and disciplines. This project advances efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM).
The project is grounded in a theoretical framework for young children's learning that focus on playful exploration, design, and building on children's own experiences and questions. The research examines how the curriculum needs to be designed to support families in data modeling, foster engagement in data modeling by both younger (ages 5-6) and older (ages 7-8) children, and provide evidence of active approaches to learning about STEM. The design and development project tests and investigates the materials using a design-based research framework. Children who participate in the workshop series should increase their confidence in solving problems, taking initiative, and drawing on available resources to pursue their own questions and respond to novel challenges. Data collected includes interviews with participants, artifacts of children's work throughout the series, and an observational instrument to document families' problem solving, persistence, and engagement with data science concepts.
Staff facilitators in museums and science centers are a critical but often overlooked component of the visitor experience. Despite assertions about the important role they play in visitor learning, there continues to be almost no research to understand staff facilitation in these settings or identify effective practices. To address these gaps, we conducted a design-based research study to describe the work of experienced museum educators and iteratively refine a model of staff facilitation to support family learning at interactive math exhibits developed through a prior project. The resulting
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 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 goal of this project is to make 21st century quantum science comprehensible and engaging to non-expert informal adult learners. This project has strong potential to add new knowledge about the public's perception and understanding of quantum physics. This scientific content is often difficult for informal audiences to grasp, and there are relatively few accessible learning resources for a non- professional audience. The development of this online, interactive resource with short animations, graphics, and simulations has strong potential to fill this gap. It will develop a visually driven online resource to engage non-expert audiences in understanding the basics of quantum physics. The web design will be modular, incorporating many multimedia elements and the structure will be flexible allowing for future expansion. All content would be freely available for educational use. There is potential for extensive reach and use of the resources by informal adult learners online as well as learners in museums, science centers, and schools. Project partners are the Joint Quantum Institute at the University of Maryland and the National Institute of Standards and Technology, College Park. An independent evaluation of the project will add new knowledge about informal learners' perceptions and/or knowledge about quantum science and technology. An initial needs assessment via focus groups with the general public will be designed to find out more about what they already know about quantum physics topics and terminology, as well as what they want to know and what formats they prefer (games, simulations, podcasts, etc.). In person user testing will be used with early versions of the project online resource using a structured think-aloud protocol. Later in year 1 and 2, online focus groups with the general public will be conducted to learn what they find engaging and what they learned from the content. Iterative feedback from participants during the formative stage will guide the development of the content and format of the online resources. The Summative Evaluation will gather data using a retrospective post-survey embedded with a pop-up link on the Atlas followed by interviews with a subset of online users. Google Analytics will be used to determine the breadth and depth of their online navigation, what resources they download, and what websites they visit afterward. A post-only survey of undergraduate and graduate students who participated in resource development will focus on changes in students' confidence around their science communication skills and level of quantum physics understanding.
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.
This proposal was submitted in response to EHR Core Research (ECR) program announcement NSF 15-509. The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education, learning, and participation.
The study will investigate the processes that connect gestures and mathematics learning. Gestures are an important yet under-investigated aspect of mathematics teaching. They can influence students' memory and understanding of mathematical representations. The series of studies will examine students' learning of the concept of mathematical equivalence by testing instruction that incorporates commonly used verbal explanations and gestures. Mathematical equivalence includes understanding the meaning of the equal sign and determining if two expressions are equal. Second and third grade children will be participants. Of particular interest in the studies is the influence of gestures on preexisting knowledge of procedures, how gestures support learning beyond emphasizing information and direct learners' attention, and the creation of procedural knowledge.
The series of experimental studies will examine the mechanisms that connect gestures and procedural understanding of mathematical equivalence. The studies begin in the first phase with examining how gesture is connected to procedural knowledge of mathematical equivalence. Subsequent studies investigate how gesture functions as a mechanism for learning beyond emphasizing or directing attention to relevant information. Data collected will students' responses to equivalence problems and eye tracking data to follow whether students are looking from one side of the equal sign to the other. In the second phase of the work, the studies will examine how gesture has beneficial effects on learning more generally in mathematics. Working memory will be assessed in order to examine the role of gesture across different individuals. Fraction tasks will be used to examine the generalization of the previous results regarding gestures to other mathematics concepts.
Purpose: Purpose: This project team will fully develop and test Teachley Connect, a platform that syncs a variety of third-party math games to give elementary schools formative assessment data and intervention support. Mobile math games provide opportunities for students to access educationally-meaningful content in and out of the classroom and to supplement instruction. There are a number of examples of math apps that show promise for supporting and assessing student learning in different areas of mathematics, yet few apps in the marketplace provide meaningful data that teachers can use. Many games provide an overall score at the end of the session, but do not help teachers know what skills students are struggling with or how to provide additional support.
Project Activities: During Phase I, (completed in 2015), the team developed a prototype of Teachley Connect, which enables the secure transfer of game and learning data between third-party math games and the Teachley servers. At the end of Phase I, researchers completed a pilot study with 20 students and two teachers and demonstrated that the prototype operated as intended with important trends indicating that the system promotes student engagement and less time spent seeking help. In Phase II, the team will add additional third party math apps to the platform, strengthen the backend management system to tag user game-play data, and build out the teacher reporting dashboard to inform instruction and identify apps to address particular student and class needs. After development is complete, the research team will conduct a larger pilot study to assess the feasibility and usability, fidelity of implementation, and the promise of the Teachley Connect for teachers to use formative assessment data to inform classroom practice, select apps to address individual student needs, and support student math learning. The study will include 12 (grade K to 3) classrooms and randomly assign them into one of three groups: 1) apps only, 2) Teachley-enabled apps, or 3) Teachley-enabled apps + data. Researchers will compare pre-and-post scores of student's math learning, classroom observations, and teacher surveys/interviews.
Product: Teachley Connect will be a mobile tablet-based platform that uses games to give elementary schools rich formative assessment data and intervention support. Teachley Connect will permit students to continue playing exactly where they left off on any tablet. The platform will also connect apps into a single teacher dashboard, providing teachers detailed reports on student performance across games, with insights for informing individual or whole group instruction. The platform will include teacher resources to support the alignment of game play with learning goals and to support implementation.