# data[].attributes.title
NSF ADVANCE Catalyst: Equity in STEM at Miami University

# data[].attributes.abstract
This NSF ADVANCE Catalyst Award to Miami University, Equity in STEM at Miami University (ESTEAM), will support data collection and self-assessment to identify systemic inequities that impact STEM faculty related to gender, race, and international status.  These inequities may be magnified during times of crisis, like the COVID- 19 pandemic. Miami University is an R2 public liberal arts university located on three local campuses in Hamilton, Middletown, and Oxford, Ohio. These campuses work to enhance the socio-economic, cultural, and racial diversity of the student body–something that benefits from diverse faculty. While Miami creates a vibrant learning community for students, the expectation for Miami faculty to be both exemplary teachers and research scholars creates demands on faculty that can lead to burnout. These demands may disproportionately impact faculty equity on the basis and intersectionality of gender, race, and international status. This project aims to identify and evaluate stressors both quantitatively and qualitatively through examination of institutional data on hiring and salaries; on institutional policies and practices on hiring, promotion and tenure, parental and family leaves, and research leaves; and on institutional tools used to evaluate teaching and research successes. Also, this project will evaluate how inequities may have been exacerbated during the COVID-19 pandemic.The goals of this project are 1) to lay the foundation for strategic recruitment and retention of faculty across gender, race, and international status by identifying barriers and structural impediments that impact STEM diversity, and 2) to set the stage for institutional transformation beyond the grant period to reduce inequities that hinder these groups of faculty during and outside of a global crisis. The research team will examine institutional data, and conduct surveys and in-person interviews. The results of this assessment aim to help reshape policies that will support retention and recruitment of diverse faculty. The data acquired in this study will be reported using multi-media methods, including technical peer-reviewed journal articles; conference presentations; and an ESTEAM website. STEM faculty at Miami will have opportunities to be involved in two programs initiated by this project: 1) An ESTEAM Faculty Learning Community that will review and discuss literature on equity in higher education, identify potential solutions for successful recruiting, retention, and promotion of diverse faculty, and develop ESTEAM Presentation Modules on STEM-diversity that can be shared with administrators, faculty, and students; and 2) a “STEM-Equity” mentoring program, which will aim to improve the climate for all STEM faculty. Results from this mentorship approach will be documented and distributed to other universities through the university’s ESTEAM website and through scholarly work. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions. Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate. ADVANCE "Catalyst" awards provide support for institutional equity assessments and the development of five-year faculty equity strategic plans at academic, non-profit institutions of higher education.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.

# data[].attributes.title
Collaborative Research: Modeling and Control of Non-Passive Networks with Distributed Time-Delays: Application in Epidemic Control

# data[].attributes.abstract
This research seeks to develop a comprehensive framework for data-driven control of large-scale networks where time delays and the corresponding complex behavior play a substantial role. An example of this situation is the ongoing COVID-19 pandemic, where these effects lead to ``reflective" spreading waves, resulting in hard to predict/control multiple phases of infection spread. To enhance pandemic preparedness and make healthcare systems and governments ready to optimally respond to potential future airborne epidemic disease, it is imperative to generate accurate network models of our connected society and disease spread. Using such realistic models, optimal control strategies can be synthesized that take into account the complex behavior caused by time delays in the network. This project will address this unmet need, which will have a significant social impact and can help stakeholders design strategies to manage a pandemic situation. Education is proactively integrated into this project at all levels, from outreach to pre-college students to graduate training. The strategy to broaden participation will leverage PIs’ connections to institutional resources and programs to help recruit students from underrepresented groups.Effective mitigation of pandemics spreading over networks requires: (a) unveiling the topology, dynamics and delays of the underlying network from experimental data; (b) use of this information to design networks that can robustly minimize the systemic effects of localized infection foci, while respecting overall minimum traffic constraints; and (c) synthesizing real-time optimal control laws that adjust local parameters to prevent the onset of delay-induced echoing waves of pandemic spread. This research seeks to achieve these objectives by embedding the problem into a more general one: data-driven control synthesis for networked systems in the presence of delay-induced non-minimum phase/non-passive behavior, in scenarios where the interconnection structure of the system may not be perfectly known a priori. This embedding allows for exploiting a rich knowledge base, ranging from non-linear identification and semi-algebraic optimization to passivity-based control of networks, leading to a computationally tractable framework. Topology identification will be accomplished through an atomic norm framework. Network synthesis will combine ideas from network control and occupation measures to design and maintain optimal topologies at a slow time scale. Real-time optimal control laws will use event-triggered passivation to prevent delay-induced instabilities.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.

# data[].attributes.title
Collaborative Research: Modeling and Control of Non-Passive Networks with Distributed Time-Delays: Application in Epidemic Control

# data[].attributes.abstract
This research seeks to develop a comprehensive framework for data-driven control of large-scale networks where time delays and the corresponding complex behavior play a substantial role. An example of this situation is the ongoing COVID-19 pandemic, where these effects lead to ``reflective" spreading waves, resulting in hard to predict/control multiple phases of infection spread. To enhance pandemic preparedness and make healthcare systems and governments ready to optimally respond to potential future airborne epidemic disease, it is imperative to generate accurate network models of our connected society and disease spread. Using such realistic models, optimal control strategies can be synthesized that take into account the complex behavior caused by time delays in the network. This project will address this unmet need, which will have a significant social impact and can help stakeholders design strategies to manage a pandemic situation.  Education is proactively integrated into this project at all levels, from outreach to pre-college students to graduate training. The strategy to broaden participation will leverage PIs’ connections to institutional resources and programs to help recruit students from underrepresented groups. Effective mitigation of pandemics spreading over networks requires: (a) unveiling the topology, dynamics and delays of the underlying network from experimental data; (b) use of this information to design networks that can robustly minimize the systemic effects of localized infection foci, while respecting overall minimum traffic constraints; and (c) synthesizing real-time optimal control laws that adjust local parameters to prevent the onset of delay-induced echoing waves of pandemic spread. This research seeks to achieve these objectives by embedding the problem into a more general one: data-driven control synthesis for networked systems in the presence of delay-induced non-minimum phase/non-passive behavior, in scenarios where the interconnection structure of the system may not be perfectly known a priori. This embedding allows for exploiting a rich knowledge base, ranging from non-linear identification and semi-algebraic optimization to passivity-based control of networks, leading to a computationally tractable framework. Topology identification will be accomplished through an atomic norm framework. Network synthesis will combine ideas from network control and occupation measures to design and maintain optimal topologies at a slow time scale. Real-time optimal control laws will use event-triggered passivation to prevent delay-induced instabilities.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.

# data[].attributes.title
Building Capacity to Develop Community-Engaged STEM Learning as an Institutional Transformative Mechanism

# data[].attributes.other_investigators[].affiliations[].name
The University of Central Florida Board of Trustees

# data[].attributes.abstract
This project aims to serve the national interest by integrating STEM learning with community-oriented problem solving, evidence-based decision making, critical thinking, and the development of civic duties. The project plans to build institutional capacity to create an immersive environment of community-engaged learning (CEL) and to transform STEM education. Working collaboratively with community partners, students will use their knowledge and skills in STEM fields to address issues facing many minority communities, particularly inequity issues related to the COVID-19 pandemic and post-pandemic rejuvenation of affected communities. This project will directly tackle the challenges faced by minority and other underserved students, including food insecurity and mental health issues, to enhance their resilience, confidence, and persistence to succeed in STEM fields. The project will have a positive impact on minority and immigrant communities in areas of health and healthcare disparity, environmental and nutritional inequity, social justice, and equal rights. The project will engage an interdisciplinary team of STEM faculty, social scientists, and student affairs staff to provide students with an immersive environment through CEL courses in both STEM and humanities, arts, and social science (HASS) fields, which will lead to sustained change and a powerful advancement in STEM education. Interdisciplinarity will be a key component in this project, as the HASS courses will allow students to understand complex societal and community issues as they apply STEM knowledge to address those issues. The STEM solutions will be placed in the social and cultural contexts of the communities. The objectives of this project include 1) to create an immersive CEL environment at NJCU that offers broad CEL-focused courses and curricula in STEM and HASS to transform the student experience in undergraduate STEM education; 2) to build a holistic support system to empower faculty, students, and communities to embrace CEL and bolster students’ success in STEM; 3) to promote NJCU as a prominent “steward of place” that benefits and contributes to the minority communities with scientific and evidence-based approaches to meet the community needs; and 4) to conduct research on interdisciplinarity and community-institution partnership, establishing evidence-based best practices for CEL and catalyzing sustainable transformation across the university and surrounding communities. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This project is also supported by the NSF IUSE: HSI program, which seeks to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs.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.

# data[].attributes.title
Collaborative Research: A New Multiscale Framework for Integrating Socio-Economic Processes, Vector-Borne Disease Control, and the Impact of Transient Events

# data[].attributes.funder_divisions[]
Mathematical and Physical Sciences (MPS)

# data[].attributes.abstract
Mathematical models for infectious diseases are critical tools for informing effective disease interventions. The disease management strategies informed by models of vector-borne diseases (such as diseases spread by mosquitoes) led to the elimination of malaria from the United States and other countries. However, malaria continues to have an enormous impact on resource-challenged regions of the world, while other existing and emerging vector-borne diseases such as West Nile Virus, Dengue Virus, and Zika Virus continue to impact both advanced and resource-challenged economies. A shortcoming of many existing modeling approaches is the absence of an explicit connection between economic factors and disease transmission – two factors that influence each other. In an increasingly connected and complex world, accounting for the synergistic effects of these factors is essential for effective disease control. For example, the COVID-19 pandemic caused sudden economic shifts that affected the progress of malaria control programs, resulting in increases in malaria-related deaths. In collaboration with an ecologist/economist and with an entomologist and a biostatistician from Africa, the investigators will build mathematical tools to bridge this gap, which will be used to inform public health and economic growth policies. The project will train graduate students through involvement in the research. Workshops on feedback between economic and disease systems will train a new generation of undergraduate students from diverse backgrounds on this important topic. This work will also involve students from Africa, a continent that is highly affected by vector-borne diseases. Incorporating socio-economic factors into vector-borne disease (VBD) systems is key to identifying new interventions, by unraveling the intertwined relationship between VBD, socio-economic conditions, and transient events. The investigators aim to develop a new mechanistic framework for transient disease dynamics that accounts for feedback between VBDs and economic systems, the impact of fast-slow time scales, and sudden endogenous and exogenous events that shift the economic landscape. Factors including heterogeneous mosquito-biting and insecticide-treated-net (ITN) replacement times, demographic structure, and human behavior in relation to ITN use will be included. For even the simplest economic-VBD model, standard methods for computing basic epidemiological metrics and performing structural identifiability analysis break down. Hence, the investigators also plan to build the mathematical toolbox required to analyze and simulate these coupled systems. Although the framework will be applied to malaria as a prototype VBD using socio-economic and epidemiological data from Kenya, Madagascar, and West Africa to calibrate the parameters and validate the outcomes of the models, it is intended to be robust enough to be applicable to other VBDs. Furthermore, the framework will allow the investigators to realistically answer important public health and economic growth questions, including how to distribute ITNs across connected populations, and how to respond to spontaneous events affecting the economic landscape.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.

# data[].attributes.title
SBIR Phase I:  An Easy-to-use Virtual Reality Development Toolkit for Education

# data[].attributes.funder_divisions[]
Technology, Innovation and Partnerships (TIP)

# data[].attributes.abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop an easy-to-use virtual reality (VR) toolkit to enable rapid development and deployment of educational VR experiences. The COVID-19 pandemic made it clear that technology to enable remote learning is critical to education at all levels. However, instructors often do not use these technologies because they lack understanding of how to meaningfully deploy it in their courses. In order for VR to be useful in education, it must not just be easy for learners to use, but also must be easy for instructors to learn to use it. This project will create a user-friendly toolkit with which educators can create targeted, subject-specific, VR educational experiences. This learning tool may become an important part of future education as it will provide access for a diverse group of students of all ages and education levels who may be unable to be physically present (due to illness, location, or other social or socio-economic obstacles) to complete courses and curricula where on-site attendance is not possible.This Small Business Innovation Research (SBIR) Phase I project fills an industry gap and unaddressed demand for educational simulations. Studies of students and instructors have shown that both want VR experiences as part of a broader demand for consumable instructional content. Developing this content, however, presents two interconnected technical hurdles: (1) developing pipelines by which existing digital resources can be used to create VR assets, and (2) creating a holistic user experience for non-technical users in the non-standardized computing environment of K-12 and higher education. Current state-of-the-art VR systems are often too complicated for non-technical users, requiring the user to have extensive programming skills, which many educators do not. This project seeks to develop methods to easily create VR assets from non-VR digital resources and a user experience (UX) structured around the “backward design” approach to instructional design. By building on the latest functionality for gestural interfaces to reflect interactions with physical spaces and objects, and developing templates to guide the instructional designer through the process, this project will enable instructional designers to develop realistic, compelling, and educationally valuable VR learning experiences.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.

# data[].attributes.title
Building Efficiency for a sustainable Tomorrow (BEST) Center

# data[].attributes.abstract
Commercial buildings are a predominant feature of U.S. cities. Such buildings form the physical infra-structure for people working in many economic sectors. The technicians who operate these buildings are largely invisible to building occupants, occasionally seen responding to a complaint about room temperature or repairing equipment. Climate change, the COVID-19 pandemic, and new digital technologies are creating a new dynamic for the work of building technicians, increasing the importance of their role, expanding the knowledge and skills required of them, and raising expectations about the quality of their work. Today’s building technicians must be prepared to manage the complex building automation, data analytics, and energy management systems of the high-performance “sustainable” buildings of the future. This project will support the BEST Center transitioning to a Resource Center to continue to serve the community for the collection, dissemination, and adoption of programs, courses, lab applications, and innovative instructional methods for the advanced technological education of building systems technicians. The BEST Resource Center will support the development of building technician education programs at community and technical colleges nationwide, engage industry to support this effort, and strengthen the national STEM pipeline. The Center will have 3 goals: 1) Transform the instructional capacity of community colleges in the field of building technician education, with an emphasis on High Performance Building Operations Technical-Professional (HPBOT-P) and Building Automation Specialist (BAS) curriculum alignment and certification; 2) Engage industry stakeholders and research partners in a national collaboration with community colleges to support high-quality building science instructional programs; and 3) Strengthen the national STEM pipeline for building technicians through outreach to high school students, women, and populations traditionally underrepresented in building science. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the Nation's economy.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.

# data[].attributes.title
A Systematic Review and Meta-Analysis on the Effectiveness of Remote Education in Math and Science

# data[].attributes.principal_investigator.affiliations[].name
American Institutes for Research in the Behavioral Sciences

# data[].attributes.awardee_organization.name
American Institutes for Research in the Behavioral Sciences

# data[].attributes.abstract
Web-based and digital tools to support math and science learning have emerged as a major trend over the past several decades. As of 2013, all 50 states and the District of Columbia offer online learning experiences to K–12 students, ranging from supplemental courses that accompany traditional in-person instruction to full-time online schools. Understanding the promise and limitations of remote learning is an urgent line of research now because of increased reliance on remote instruction during the COVID-19 pandemic. The pandemic and continued disruptions to schooling have been associated with substantial setbacks in learning. Studies examining academic progress between March 2020 and March 2021 reported significant lack of progress, which was particularly pronounced in math and science relative to reading. Furthermore, lack of progress was greatest among subgroups that already experience achievement disparities in math and science, such as students of color, rural students, and those eligible for free or reduced-price lunch.This comprehensive systematic review and meta-analysis synthesizes evidence surrounding math and science remote education programs from the past 15 years. The goal is to understand the effectiveness of math and science remote education programs; how their effectiveness varies by program characteristics (e.g., fully online vs. hybrid, synchronous vs. asynchronous, and student-instructor ratio); and whether their effects vary with student sample characteristics. To prioritize future research needs, the researchers will create evidence gap maps to illuminate patterns in the existing evidence base and identify areas in which evidence is lacking. This analysis will build foundational knowledge and inform educators about which programs and strategies in math and science remote education have been most effective. In addition, the review will provide targeted guidance on which programs and strategies have been most appropriate for vulnerable groups of students.The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.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.

# data[].attributes.title
Collaborative Research: A New Multiscale Framework for Integrating Socio-Economic Processes, Vector-Borne Disease Control, and the Impact of Transient Events

# data[].attributes.funder_divisions[]
Mathematical and Physical Sciences (MPS)

# data[].attributes.abstract
Mathematical models for infectious diseases are critical tools for informing effective disease interventions. The disease management strategies informed by models of vector-borne diseases (such as diseases spread by mosquitoes) led to the elimination of malaria from the United States and other countries. However, malaria continues to have an enormous impact on resource-challenged regions of the world, while other existing and emerging vector-borne diseases such as West Nile Virus, Dengue Virus, and Zika Virus continue to impact both advanced and resource-challenged economies. A shortcoming of many existing modeling approaches is the absence of an explicit connection between economic factors and disease transmission – two factors that influence each other. In an increasingly connected and complex world, accounting for the synergistic effects of these factors is essential for effective disease control. For example, the COVID-19 pandemic caused sudden economic shifts that affected the progress of malaria control programs, resulting in increases in malaria-related deaths. In collaboration with an ecologist/economist and with an entomologist and a biostatistician from Africa, the investigators will build mathematical tools to bridge this gap, which will be used to inform public health and economic growth policies. The project will train graduate students through involvement in the research. Workshops on feedback between economic and disease systems will train a new generation of undergraduate students from diverse backgrounds on this important topic. This work will also involve students from Africa, a continent that is highly affected by vector-borne diseases. Incorporating socio-economic factors into vector-borne disease (VBD) systems is key to identifying new interventions, by unraveling the intertwined relationship between VBD, socio-economic conditions, and transient events. The investigators aim to develop a new mechanistic framework for transient disease dynamics that accounts for feedback between VBDs and economic systems, the impact of fast-slow time scales, and sudden endogenous and exogenous events that shift the economic landscape. Factors including heterogeneous mosquito-biting and insecticide-treated-net (ITN) replacement times, demographic structure, and human behavior in relation to ITN use will be included. For even the simplest economic-VBD model, standard methods for computing basic epidemiological metrics and performing structural identifiability analysis break down. Hence, the investigators also plan to build the mathematical toolbox required to analyze and simulate these coupled systems. Although the framework will be applied to malaria as a prototype VBD using socio-economic and epidemiological data from Kenya, Madagascar, and West Africa to calibrate the parameters and validate the outcomes of the models, it is intended to be robust enough to be applicable to other VBDs. Furthermore, the framework will allow the investigators to realistically answer important public health and economic growth questions, including how to distribute ITNs across connected populations, and how to respond to spontaneous events affecting the economic landscape.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.

# data[].attributes.funder_divisions[]
Social, Behavioral, and Economic Sciences (SBE)

# data[].attributes.abstract
Many disability communities have developed remote techniques for participating in work and social life. These innovative techniques are part of disability culture and community. Many people believe that technologies merely help disabled people do the things that nondisabled people do. However, disabled people also design their own technologies in order to challenge what is considered normal. “Disability Culture and Technology” aims to address the social inequalities that disabled people face by documenting the role of technology in disability communities. The data resulting from this study will be made free and available to the public. Publications resulting from this data will also enable policymakers, employers, and decision-makers to make more informed choices regarding remote participation. This will make employment and education opportunities more equitable and diverse. Society will also benefit from a better understanding of disabled peoples’ contributions and knowledge.“Disability Culture and Technology” will ask how disabled people use and transform remote access technologies to shape society, both during and before the COVID-19 pandemic. This NSF Scholars Award gathers oral histories, images, videos, and other forms of documentation to build an archive of the ways that disabled people use remote access to form community. This archive will enable researchers to study how disabled people shape technology in order to survive the pandemic, and more broadly, to change society. It will inform how science and technology studies scholars understand issues such as who designs, uses, and transforms technology. The research and resulting publications will also inform how disability studies scholars understand the role of technology in connecting disabled people across long distances and forming communities.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.