Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&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=1397&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-funder" }, "data": [ { "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 } }, { "type": "Grant", "id": "15099", "attributes": { "award_id": "2417341", "title": "Strategic interventions in first-year Engineering to overcome social-emotional learning barriers", "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": 29310, "first_name": "Christine", "last_name": "Delahanty", "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": 458371, "principal_investigator": { "id": 31644, "first_name": "Maureen", "last_name": "Tang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31642, "first_name": "Dimitri", "last_name": "Papadopoulos", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31643, "first_name": "Jennifer S", "last_name": "Atchison", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by developing and providing novel social emotional learning (SEL) support and skill development for first-year undergraduate engineering students. COVID-related learning losses have exacerbated the existing structural inequities in K-12 education leaving a growing number of students ill-prepared for the rigors of university life. Included in these learning losses are the social-emotional skills required for college and career success in the 21st century. To support students as they develop these skills, the project team plans to integrate SEL interventions and structural changes into first-year mathematics and engineering courses and to analyze the efficacy of these efforts. Through the collaborative efforts of faculty from engineering and mathematics, this project will develop and assess remedial and preventative interventions that will directly impact 400 - 500 students/year. <br/><br/>First, a novel remedial intervention in first-year math courses will encourage struggling students to reflect not just on technical content but also on the barriers they face, the personal support networks to which they have access, and the extent to which they utilize the academic supports available to them. Second, the project team intends to develop and assess preventative SEL interventions by modifying an existing 1-unit first-year seminar. Offering cohorts by major will introduce first-year students to peers, faculty, and upperclassmen for earlier, stronger engineering identity formation. Modifying the curriculum to include SEL instruction will encourage introspection and self-awareness. Third, the project team will complete qualitative research by analyzing student artifacts and conducting interviews with high-risk students who succeed despite low incoming math scores. Results will be assessed via quantitative comparison of grades and SEL instruments scores pre/post interventions, between test/control course sections, and against historical data. The results of this study will provide generalizable understanding of how first-year engineering students develop SEL skills and how they can be measured. Results will be disseminated internally and externally through peer-reviewed publications, professional societies, and personal networks. <br/><br/>The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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 } }, { "type": "Grant", "id": "15100", "attributes": { "award_id": "2337088", "title": "Collaborative Research: Point-of-Care Additive Manufacturing for Health: Cultivating and Assessing Engineering Students' Technical Knowledge and Professional Skills", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [ { "id": 2088, "first_name": "Jennifer", "last_name": "Ellis", "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": 374146, "principal_investigator": { "id": 31646, "first_name": "Weilong", "last_name": "Cong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31645, "first_name": "Patricia A", "last_name": "Maloney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 270, "ror": "https://ror.org/0405mnx93", "name": "Texas Tech University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project serves the national interest by preparing a qualified engineering workforce with important technical and professional skills for the health-based point-of-care (POC) additive manufacturing (AM) industry. Health-based POC-AM is a non-traditional form of manufacturing referring to the just-in-time creation of anatomical models, surgical instruments, prosthetics, scaffolds, etc., based on medical imaging data and need at the place of patient care. The growth of POC-AM requires the collaboration of medical, engineering, and social science professionals in that engineers must be trained to be socially adept and communicative about additive manufacturing specifically for healthcare applications. Despite the exponential growth in POC-AM market value and scholarly activities, the needed education and training components are underdeveloped, especially for undergraduate students in public engineering schools. This IUSE Engaged Student Learning Level 2 project will bridge this talent gap by creating an undergraduate engineering course that is broadly accessible and will be able to define, cultivate, and assess students' technical and professional skills needed by the booming POC-AM industry. This project features a project-based learning plan to develop students' theoretical and hands-on skills to create a broad range of medical objects from non-patient-specific personal protection equipment and anatomical models to patient-specific prosthetics, tissues, and implants. This project will strongly emphasize the development of students' reflective communication skills, both written and verbal, with colleagues in both engineering and in healthcare. The project will also design a protocol for assessing and developing those communication skills using objective and subjective metrics.<br/><br/>Thus, the goal of this project is to remove barriers between POC-AM research and education while interconnecting key concepts in multiple related sub-disciplines through teaching this unique skillset to undergraduate students at two large public universities. The innovative course that focuses on students' technical and communication skills development will train holistic and well-rounded engineering students who can solve complex problems that require a broad integration of technical knowledge and communication skills. The combination of cutting-edge learning about POC-AM and a targeted and efficient communication skills development targeted to the needs of the post-COVID student population makes this project highly effective for undergraduate education. The developed instructional and assessment materials will be publicly available as this project can be a model for other similar upper division engineering courses, especially in an emerging and practical field. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is jointly funded by the Established Program to Stimulate Competitive Research. This project is jointly funded by IUSE and the Established Program to Stimulate Competitive Research (EPSCoR).<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": "15101", "attributes": { "award_id": "2337087", "title": "Collaborative Research: Point-of-Care Additive Manufacturing for Health: Cultivating and Assessing Engineering Students' Technical Knowledge and Professional Skills", "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": 2088, "first_name": "Jennifer", "last_name": "Ellis", "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": 316109, "principal_investigator": { "id": 31648, "first_name": "Meng", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31647, "first_name": "Xiuzhi S", "last_name": "Sun", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "This project serves the national interest by preparing a qualified engineering workforce with important technical and professional skills for the health-based point-of-care (POC) additive manufacturing (AM) industry. Health-based POC-AM is a non-traditional form of manufacturing referring to the just-in-time creation of anatomical models, surgical instruments, prosthetics, scaffolds, etc., based on medical imaging data and need at the place of patient care. The growth of POC-AM requires the collaboration of medical, engineering, and social science professionals in that engineers must be trained to be socially adept and communicative about additive manufacturing specifically for healthcare applications. Despite the exponential growth in POC-AM market value and scholarly activities, the needed education and training components are underdeveloped, especially for undergraduate students in public engineering schools. This IUSE Engaged Student Learning Level 2 project will bridge this talent gap by creating an undergraduate engineering course that is broadly accessible and will be able to define, cultivate, and assess students' technical and professional skills needed by the booming POC-AM industry. This project features a project-based learning plan to develop students' theoretical and hands-on skills to create a broad range of medical objects from non-patient-specific personal protection equipment and anatomical models to patient-specific prosthetics, tissues, and implants. This project will strongly emphasize the development of students' reflective communication skills, both written and verbal, with colleagues in both engineering and in healthcare. The project will also design a protocol for assessing and developing those communication skills using objective and subjective metrics.<br/><br/>Thus, the goal of this project is to remove barriers between POC-AM research and education while interconnecting key concepts in multiple related sub-disciplines through teaching this unique skillset to undergraduate students at two large public universities. The innovative course that focuses on students' technical and communication skills development will train holistic and well-rounded engineering students who can solve complex problems that require a broad integration of technical knowledge and communication skills. The combination of cutting-edge learning about POC-AM and a targeted and efficient communication skills development targeted to the needs of the post-COVID student population makes this project highly effective for undergraduate education. The developed instructional and assessment materials will be publicly available as this project can be a model for other similar upper division engineering courses, especially in an emerging and practical field. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is jointly funded by the Established Program to Stimulate Competitive Research. This project is jointly funded by IUSE and the Established Program to Stimulate Competitive Research (EPSCoR).<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": 1385, "pages": 1397, "count": 13961 } } }