Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=-funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-funder" }, "data": [ { "type": "Grant", "id": "15089", "attributes": { "award_id": "2436592", "title": "The National Welding Hub for Advanced Welding Process Education and Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 692, "first_name": "Virginia", "last_name": "Carter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 2425104, "principal_investigator": { "id": 31624, "first_name": "Monica", "last_name": "Pfarr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 27152, "first_name": "Michael D", "last_name": "Fox", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31623, "first_name": "W. Richard", "last_name": "Polanin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2339, "ror": "https://ror.org/035aej353", "name": "Lorain County Community College", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Welding and materials joining is an essential technology used across numerous industries. Throughout the US, the largest employers of welders and welding technicians include commercial building construction, infrastructure, agricultural equipment manufacturing, automotive manufacturing, oil and gas, shipbuilding, aerospace, energy, and metal fabrication. According to the Occupational Data Report developed by Lightcast in 2023, there's a projected need for 330,000 new welding professionals by 2028, or 82,500 annually between 2024-2028. A recent survey conducted by Weld-Ed found that enrollment has declined 8.7% in welding programs since the end of the pandemic. Yet the welding industry is rapidly evolving, driven by technological advancements from increased automation to new material usage. There has been a significant increase in the use of robotic welding systems that offer unparalleled precision, efficiency and consistency. The integration of artificial intelligence in these systems to allow for real-time adjustments and decision-making will only enhance their use. With the documented need for welders, the Weld-Ed Hub proposes to continue to support welding programs to ensure that industries will have the skilled technical welders needed.<br/><br/>The Weld-Ed Hub will provide fundamental welding technology, emerging welding technology, industry and education research data, best practice teaching methods to welding instructors and industry professionals. The Weld-Ed Hub project goal is to improve the number and quality of welding and materials joining technicians to meet industry workforce need. To attain this goal a series of objectives and activities will be supported, including: 1) Providing faculty professional development activities to improve the ability of welding instructors and welding programs to prepare welding technicians for the workforce; 2) Gathering and disseminating advanced material welding processes, emerging welding technology, and advanced inspection technology to welding instructors; 3) Recruiting new welding students and supporting the retention of welding students through career awareness, career guidance, and career assistance; and 4) Conducting ongoing research to determine the current and future state of welding and inspection technology and education and training delivery. 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.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15090", "attributes": { "award_id": "2436120", "title": "MPOPHC: Integrating human risk perception and social processes into policy responses in an epidemiological model", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "OFFICE OF MULTIDISCIPLINARY AC" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 1344200, "principal_investigator": { "id": 31626, "first_name": "Brian", "last_name": "Beckage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1138, "first_name": "Suzanne", "last_name": "Lenhart", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 8904, "first_name": "Charles B", "last_name": "Sims", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31625, "first_name": "Katherine M", "last_name": "Lacasse", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 433, "ror": "", "name": "University of Vermont & State Agricultural College", "address": "", "city": "", "state": "VT", "zip": "", "country": "United States", "approved": true }, "abstract": "Epidemics arise from interactions between pathogens and human hosts, where the pathogen influences human behavior and human behavior influences the spread of the pathogen. The models used to predict pathogen spread do not include the complexity of interactions between disease and human behavior but instead focus on biological processes and policy interventions. However, disease transmission depends on people’s behaviors, which are shaped by their perceptions of risk from the disease and from health interventions, as well as by the opinions and behaviors of the other people around them. This project will contribute to the development of mathematical epidemiological models that better represent the complexities of the human response to disease and that can be used to evaluate the relative impacts of public health policies on disease dynamics. The project will be focused on understanding respiratory diseases such as COVID-19, seasonal flu, and bird flu, but can be readily modified to be broadly applicable to other infectious diseases such as HIV or Ebola. The project will contribute to existing national COVID-19 and Flu Scenario Modeling Hubs that are working to better predict and understand the dynamics of infectious disease and to contribute to policy interventions. The Investigators will disseminate the results and foster connections with the disease modeling community through a workshop for public health professionals and will engage the public through production of educational music videos targeted at the broader community<br/><br/>The complexity of human behavior is not well represented in epidemiological models, contributing to reduced skill and utility of model forecasts. While some epidemiological models represent human behavioral responses using a few static parameters, the Investigators will construct models of human behavior and policy processes that update dynamically to represent the dependence of human responses to the evolving state of the epidemic. Human cognition, social and policy responses will be represented using a system of differential equations linked with a traditional Susceptible-Exposed-Infected-Recovered epidemiological model using infectious respiratory diseases such as SARS-CoV-2 and H5N1 as model systems. Adoption of protective behaviors (vaccination, physical distancing) will be a function of risk perceptions (from disease and health interventions), health policies (lockdowns, vaccine mandates), and the behavior of other people (social norms). Policy interventions and adoption of protective behaviors mediate disease spread and impacts (infections and deaths) that influence human behavioral and policy responses. Mathematical novelty arises because cognition depends upon the history of infection, so the differential equations have past-dependence, generating differential integral equations. Model outputs will be used to analyze the sensitivity of and uncertainty in epidemic forecasts that arise from human risk perceptions, social influence, protective behaviors, and policy interventions. This project will advance the disease modeling community’s capability to analyze the interlinked dynamics of human social systems and infectious disease, increase the impact of social science on the disease modeling community, and will develop analysis methods for the complex and time-dependent interactions that arise from linkages of disease dynamics with social systems. <br/><br/>This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15091", "attributes": { "award_id": "2346335", "title": "Collaborative Research: CIRC: New: Facilitating Language Technologies for Crisis Preparedness and Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CCRI-CISE Cmnty Rsrch Infrstrc" ], "program_reference_codes": [], "program_officials": [ { "id": 31622, "first_name": "Cindy", "last_name": "Bethel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 584184, "principal_investigator": { "id": 31628, "first_name": "Fei", "last_name": "Xia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31627, "first_name": "William D", "last_name": "Lewis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Language technologies are promising and could have strong impact during disaster responses. they can help to triage text messages in a disaster to determine what aid to provide. Language technologies can translate vast amounts of data related to an ongoing pandemic. Responders can use these technologies to converse with victims during disaster responses. However, advances in language technologies to date are limited. They focus on a few dozen of the more than 6500 languages spoken or signed in the world today. Current language technologies neglect millions of people. This especially impacts those who are most at risk for experiencing disasters. This project provides an infrastructure for language technology advancements for crisis response. The results will be useful for everyone, no matter the language they speak.<br/><br/>This project builds datasets of crisis communications using dedicated data collections and social media harvesting. These datasets will be applicable to curated crisis scenarios. They will use common language scenarios necessary to communicate with vulnerable populations. This approach helps people for whom language technologies are not typically developed. The project will bring together researchers from different disciplines. These include language technology researchers, experts in disaster relief, linguistics, and human-computer interaction. The project will target representatives from the local speech communities to take part. To coordinate this effort, the project will organize yearly workshops and shared tasks with the communities.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15092", "attributes": { "award_id": "2406340", "title": "SCH: Integrating DNA Nanotechnology and CMOS Electronics for Next-generation Diagnostics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Smart and Connected Health" ], "program_reference_codes": [], "program_officials": [ { "id": 27137, "first_name": "Sorin", "last_name": "Draghici", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 1200000, "principal_investigator": { "id": 31630, "first_name": "Jun-Chau", "last_name": "Chien", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31629, "first_name": "Grigory", "last_name": "Tikhomirov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Currently, healthcare relies on annual blood and urine tests that often miss early signs of developing diseases. This project will develop innovative sensing technologies that will enable more frequent blood and urine testing, as well as testing for newly discovered disease-associated molecules, which will improve the likelihood of detecting diseases early. This project aims to combine the strengths of microelectronics and emerging molecular nanotechnology to develop sensing platforms that are highly sensitive and easy-to-use. If successful, in the future, rather than waiting for annual checkups, our health will be systematically and comprehensively monitored frequently throughout the day. The research is integrated with an educational and outreach program that introduces these technologies to students from K-12 to the graduate level, providing training and education opportunities for future health personnel. The results of this project will lead to future work that could greatly reduce healthcare costs and vastly improved health outcomes. <br/><br/>Although continuous glucose monitoring and rapid COVID-19 tests serve as outstanding currently available examples of biomarker sensing, general purpose integration of biomarker sensing into daily life remains challenging. The challenges include (i) the lack of sensors that can offer signals at low detection limits with high specificity while requiring minimal sample preparation prior to sensing, (ii) the absence of appropriate electronic interfaces to convert low target-sensor interactions into detectable signals within a miniaturized platform that can be easily worn or carried, and (iii) significant measurement variation from different samples. This research aims to overcome the challenges by integrating biosensors enabled by DNA nanotechnology with miniaturized yet high-performance complementary metal-oxide-semiconductor (CMOS) electronics to develop new diagnostic platforms that offer enhanced sensitivity, specificity, throughput, and analyte scope. Specifically, the team will: (1) develop molecular engineering techniques to perform in-situ signal amplification for target-binding aptamer biosensors using DNA origami nanotechnology; (2) develop advanced biosensor-interfacing circuits that overcome noise/power limitations and offer near shot-noise-limited sensitivity; (3) create integration methodologies of molecules with electronics that facilitate the site-specific scalable functionalization of biosensors, enabling multiplexed detection across a scalable array with aptamers of different sequences; and (4) implement a wireless wearable device for continuous monitoring of molecules in interstitial fluids, alongside a CMOS/microfluidics system designed for blood biomarker analysis. Additionally, machine learning and data fusion techniques will be incorporated to enhance the accuracy of molecular quantification with minimal calibration when measuring complex fluids from various types and individuals. The integration of these platforms will enable continuous or more frequent sampling of specific biomarkers by users. This longitudinal data pattern paves the way for early disease detection and offers an improved alternative to current healthcare practices.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15093", "attributes": { "award_id": "2416741", "title": "Investigating the Uptake of Research-Based Instructional Strategies: A Post-COVID Update", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 27310, "first_name": "James A. M.", "last_name": "Alvarez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 156614, "principal_investigator": { "id": 31631, "first_name": "Estrella", "last_name": "Johnson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices.<br/><br/>The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15094", "attributes": { "award_id": "2427070", "title": "SCC-PG: Proactive Community Health Protection Against Airborne Diseases through Intelligent and Timely Pathogen Monitoring and Communication", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 27022, "first_name": "Vishal", "last_name": "Sharma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 150000, "principal_investigator": { "id": 31634, "first_name": "Na", "last_name": "Wei", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26937, "first_name": "Dong", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 28438, "first_name": "Vishal", "last_name": "Verma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31632, "first_name": "Thanh H", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31633, "first_name": "Eric L", "last_name": "Morgan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Infectious airborne pathogens and their spread have caused increased incidences of outbreaks and pandemics, which challenge our society with severe impacts on public health, economies, and social well-being. Controlling airborne pathogens is challenging because they are transmitted easily via respiratory aerosols, spread quickly over large distances, and can mutate to become more infectious and morbid in a short time. Reactive approaches such as treating illnesses or responding to outbreaks after they occur are usually ineffective and costly. In contrast, proactively addressing airborne pathogens is of paramount importance for minimizing the likelihood of outbreaks and safeguarding public health. A proactive approach in combating airborne diseases necessitates early detection of target pathogens and effective communication to ensure prompt collective action within communities. However, it remains extremely challenging to achieve rapid, accurate, scalable, and cost-effective on-site detection of airborne pathogens. Meanwhile, along with the deployment of the pathogen sensing technologies, there are important questions to address on how to communicate potential risks and mitigation options to communities. To address these challenges, this SCC-PG project brings together a highly interdisciplinary team of researchers and uses community-based participatory research to engage a broad range of community partners. Success of this SCC-PG project will provide innovative and proactive solutions in response to the grand challenge of public health protection. Such a proactive system will also foster a culture of prevention and preparedness to better handle future community health threats.<br/><br/>The research will significantly advance science and technology in designing, constructing, modeling and deploying a novel biosensing system timely in-field airborne pathogen monitoring. The intellectual contributions are three-fold. First, the project will create an intelligent system for automated, real-time, and multiplex monitoring of critical airborne pathogens. In addition, an intelligent automated system to obtain time-resolved measurements of infectious aerosols in-field will be established. Second, the project will create a novel data-driven quality-aware deep learning approach which not only effectively generates inference and reconstruction results but also provides rigorous accuracy quantification and interpretation. Third, the project will develop a new framework of ethical dialogue to provide collaborative spaces that will lead to greater involvement and empowerment of the public with the focus on effective risk communication and mitigation.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15095", "attributes": { "award_id": "2416742", "title": "Investigating the Uptake of Research-Based Instructional Strategies: A Post-COVID Update", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 27310, "first_name": "James A. M.", "last_name": "Alvarez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 113972, "principal_investigator": { "id": 31635, "first_name": "Naneh", "last_name": "Apkarian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices.<br/><br/>The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15096", "attributes": { "award_id": "2403380", "title": "Collaborative Research: SHF: Medium: SCIOPT: Toward Certifiable Compression-Aware SciML Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 272992, "principal_investigator": { "id": 31636, "first_name": "Martin", "last_name": "Burtscher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The future of science-enabled discoveries critically relies on the speed of high-performance simulations conducted at large scales and high resolutions. Unfortunately, lacking such performance and scale, current approaches cannot keep up with the backlog of problems in areas of paramount societal consequence, such as climate science and the spread of pandemics. A principal reason for these shortfalls is the rising cost of moving huge amounts of simulation data between supercomputer memories and processors – a cost that increasingly dwarfs the time spent in actual computations. Thus, developing techniques to reduce the volume of data exchanged without sacrificing accuracy is key to future progress in computation-enabled research. Such data reduction is even more important in the emerging area of Scientific Machine Learning (SciML), where simulations are assisted by artificial intelligence (AI) based surrogate models, an area where the data exchange needs are often much higher. The investigators’ expertise in scientific machine learning, data compression, compilers, and program correctness will be central in our collaboration to help SciOPT achieve its goal of fast and reliable AI-assisted scientific simulations. The impact of this project will be to establish new technologies that reduce data volume without sacrificing accuracy in both high-performance computing and the emerging area of SciML. These technologies, in turn, translate directly into societal benefits such as improved healthcare and safer environments. The project will broaden participation in this area through undergraduate research plans that reach out to students from groups underrepresented in computing.<br/><br/>This research project, entitled SciOPT, will principally rely on data compression to reduce the amount of data moved: simulation data will be compressed before transmission and decoded upon reception before applying computations. The investigators will also pursue the potentially even more impactful approach of compressing the data and applying computations directly on the compressed data. SciOPT will evaluate both of these approaches in the context of challenging SciML applications that are currently bottlenecked by data exchanges. To ensure higher degrees of automation and productivity, SciOPT will develop efficient compiler-based methods to manage compressed data layout and locality. Moreover, it will automatically generate high-speed compression algorithms that are tailored to the data. To ensure the veracity of the computational results produced by these compressed-data simulations, SciOPT will include rigorous correctness-checking methods at multiple stages to guard the overall simulation workflows.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15097", "attributes": { "award_id": "2403379", "title": "Collaborative Research: SHF: Medium: SCIOPT: Toward Certifiable Compression-Aware SciML Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 819000, "principal_investigator": { "id": 31639, "first_name": "Ganesh", "last_name": "Gopalakrishnan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31637, "first_name": "Mary", "last_name": "Hall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31638, "first_name": "Varun", "last_name": "Shankar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "The future of science-enabled discoveries critically relies on the speed of high-performance simulations conducted at large scales and high resolutions. Unfortunately, lacking such performance and scale, current approaches cannot keep up with the backlog of problems in areas of paramount societal consequence, such as climate science and the spread of pandemics. A principal reason for these shortfalls is the rising cost of moving huge amounts of simulation data between supercomputer memories and processors – a cost that increasingly dwarfs the time spent in actual computations. Thus, developing techniques to reduce the volume of data exchanged without sacrificing accuracy is key to future progress in computation-enabled research. Such data reduction is even more important in the emerging area of Scientific Machine Learning (SciML), where simulations are assisted by artificial intelligence (AI) based surrogate models, an area where the data exchange needs are often much higher. The investigators’ expertise in scientific machine learning, data compression, compilers, and program correctness will be central in our collaboration to help SciOPT achieve its goal of fast and reliable AI-assisted scientific simulations. The impact of this project will be to establish new technologies that reduce data volume without sacrificing accuracy in both high-performance computing and the emerging area of SciML. These technologies, in turn, translate directly into societal benefits such as improved healthcare and safer environments. The project will broaden participation in this area through undergraduate research plans that reach out to students from groups underrepresented in computing.<br/><br/>This research project, entitled SciOPT, will principally rely on data compression to reduce the amount of data moved: simulation data will be compressed before transmission and decoded upon reception before applying computations. The investigators will also pursue the potentially even more impactful approach of compressing the data and applying computations directly on the compressed data. SciOPT will evaluate both of these approaches in the context of challenging SciML applications that are currently bottlenecked by data exchanges. To ensure higher degrees of automation and productivity, SciOPT will develop efficient compiler-based methods to manage compressed data layout and locality. Moreover, it will automatically generate high-speed compression algorithms that are tailored to the data. To ensure the veracity of the computational results produced by these compressed-data simulations, SciOPT will include rigorous correctness-checking methods at multiple stages to guard the overall simulation workflows.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15098", "attributes": { "award_id": "2417294", "title": "Fostering Engaged Team-Based Learning in Asynchronous Online and Hybrid Learning Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 1869, "first_name": "Thomas", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-15", "end_date": null, "award_amount": 400000, "principal_investigator": { "id": 31641, "first_name": "Sy-Miin", "last_name": "Chow", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31640, "first_name": "Prabhani Kuruppumullage", "last_name": "Don", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by developing and evaluating T-PRACTISE (Team-Based Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education), a team-based framework for fostering engaged online asynchronous learning among diverse students, such as adult learners. These students may be pursuing higher education asynchronously while balancing myriad work-, family-, and life-related demands in online and hybrid environments. This project addresses some of the long-standing challenges in online and hybrid learning made salient by the COVID-19 pandemic and subsequent recovery. The project plans to develop and evaluate T-PRACTISE, a set of tools that integrate current e-learning technologies with online team-based learning activities. This work aims to identify and circumvent training disparities through: (1) timely identification and delivery of remedial actions to develop pre-requisite skills; (2) an innovative algorithm to determine and provide personalized dosages of training and engagement exercises; and (3) extraction and evaluation of engagement and learning/teaching outcomes across multiple iterations and time scales to elucidate possible determinants of changes in learning/teaching and engagement outcomes.<br/><br/>This projects targets 1000 undergraduate students from online, hybrid, and in-person degree programs and is pursuing three goals. First, is to design, implement, and pilot a proof-of-concept T-PRACTISE system to reduce training and engagement disparities in an asynchronous online environment. Second, is to adapt and extend T-PRACTISE to hybrid courses with a range of environmental characteristics (e.g., student, instructor, and course). Third, is to evaluate students’ and instructors’ changes in learning/teaching outcomes, engagement, and possible moderating effects of environmental characteristics. To enhance quality control, project deliverables are refined by incorporating feedback from students, instructors, and an independent external evaluator. Efficacy of didactic activities aimed at circumventing students’ and their teams’ training disparities is evaluated based on ongoing computerized adaptive assessment results, real-time learning and engagement analytic data from T-PRACTISE and extant e-learning tools, and inclusion of students’ engagement data into the training recommendations devised by the learning algorithm in T-PRACTISE. The utility of the T-PRACTISE system will be evaluated across a variety of fully and partially online classes of different sizes for transferability and scalability. The expanded web app and resources will be made freely available to the broader research community to encourage cross-instructor, cross-departmental, and cross-institution exchanges of students’ training strategies to enhance the future of personalized and team-based higher instruction. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.<br/><br/>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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1405, "pages": 1419, "count": 14184 } } }