| NSF Org: |
DRL Division of Research on Learning in Formal and Informal Settings (DRL) |
| Recipient: |
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| Initial Amendment Date: | September 3, 2024 |
| Latest Amendment Date: | September 3, 2024 |
| Award Number: | 2418739 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Paul Tymann
ptymann@nsf.gov (703)292-2832 DRL Division of Research on Learning in Formal and Informal Settings (DRL) EDU Directorate for STEM Education |
| Start Date: | September 15, 2024 |
| End Date: | August 31, 2027 (Estimated) |
| Total Intended Award Amount: | $899,799.00 |
| Total Awarded Amount to Date: | $899,799.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
550 S COLLEGE AVE NEWARK DE US 19713-1324 (302)831-2136 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
220 HULLIHEN HALL NEWARK DE US 19716-0099 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Cyberlearn & Future Learn Tech |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
The U.S. Bureau of Labor Statistics 2019-29 employment projections show that occupations in STEM fields are expected to grow 8.0 percent by 2029, compared with 3.7 percent for all occupations. Computing occupations as a group are projected to grow about 3 times as fast as the average between 2019 and 2029 at 11.5 percent resulting in slightly more than half a million new computing jobs over the 10-year period. Despite efforts to increase learner performance in introductory computing courses, studies have shown only a slight decline in failure rates. The goal of this project is to explore the use of generative AI to reduce instructor workload and to improve student learning in introductory computing courses by providing real-time, personalized feedback for students. The LAPIS (Learner-Adaptive, Pedagogical Interactive Solutions) system will use generative AI to provide real-time, personalized feedback for students spending too much time mastering a topic. For instructors, it will offer critical insights through visual dashboards, allowing them to manage introductory computing courses at scale.
The project will focus on optimizing intervention data representation, determining critical student information for personalized feedback, and understanding the impact of feedback variations on student outcomes and benefits to instructors and course staff. Research conducted in this project will focus on (1) representing introductory computing course data for intervention opportunities, (2) determining necessary student information for personalized feedback, and (3) understanding how feedback variation influences student outcomes. The evaluation of LAPIS will utilize persona creation, rubric revision, A/B testing, variation in LAPIS implementation, surveys, interviews, and think-aloud sessions. A development panel of educators and CS professionals will serve as the initial users of LAPIS, ensuring LAPIS design aligns with various user abilities and motivations. An evaluation advisory board, consisting of experts in related fields, will assess project progress, methods, effectiveness and feasibility.
This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning.
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.
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