Abstract & Details
Description
Award ID: 2036502
This Small Business Innovation Research Phase II project seeks to develop a Personalized Text Simplification (PeTeS) technology to be used asa Computer Assisted Tool for students with learning difficulties and/or disabilities. Several automatic text simplification tools are available in the market. Unfortunately, the existing tools are one-size-fits-all solutions offering no personalization and not supporting the teachers instructional goals. The innovation of PeTeS is in the use of Machine Learning and Natural Language Processing algorithms to perform automatic text simplification customizing texts for each student, enabling him/her to understand the curriculum and improve vocabulary knowledge at the same time. PeTeS will be compatible with both independent use by students and teacher-driven data-driven instruction. PeTeS will be usable both in class and in remote education settings, which is a new critical demand in our schools caused by COVID-19 pandemic. The objective of this Phase II Project is to fully develop the PeTeS product and evaluate its effectiveness in classroom and remote education settings. The objective of this Phase II Project is to develop PeTeS a Personalized Text Simplification tool. PeTeS will enable teachers of material in courses such as education, reading, language, and literacy coaches to provide personalized reading accommodations and intervention to their students to improve their reading skills by automatically simplifying the text to match the individual students knowledge. PeTeS will be compatible with both independent use by students and teacher-driven data-driven instruction. PeTeS will be usable both in class and in remote education settings, which is a new critical demand in our schools caused by COVID-19 pandemic. 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.
NSF Program Director: Rajesh Mehta
This Small Business Innovation Research Phase II project seeks to develop a Personalized Text Simplification (PeTeS) technology to be used asa Computer Assisted Tool for students with learning difficulties and/or disabilities. Several automatic text simplification tools are available in the market. Unfortunately, the existing tools are one-size-fits-all solutions offering no personalization and not supporting the teachers instructional goals. The innovation of PeTeS is in the use of Machine Learning and Natural Language Processing algorithms to perform automatic text simplification customizing texts for each student, enabling him/her to understand the curriculum and improve vocabulary knowledge at the same time. PeTeS will be compatible with both independent use by students and teacher-driven data-driven instruction. PeTeS will be usable both in class and in remote education settings, which is a new critical demand in our schools caused by COVID-19 pandemic. The objective of this Phase II Project is to fully develop the PeTeS product and evaluate its effectiveness in classroom and remote education settings. The objective of this Phase II Project is to develop PeTeS a Personalized Text Simplification tool. PeTeS will enable teachers of material in courses such as education, reading, language, and literacy coaches to provide personalized reading accommodations and intervention to their students to improve their reading skills by automatically simplifying the text to match the individual students knowledge. PeTeS will be compatible with both independent use by students and teacher-driven data-driven instruction. PeTeS will be usable both in class and in remote education settings, which is a new critical demand in our schools caused by COVID-19 pandemic. 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.
NSF Program Director: Rajesh Mehta
| Status | Closed |
|---|---|
| Effective start/end date | 06/15/21 → 11/30/25 |
Funding
- SBIR Phase II: $1,000,000.00
Active Fiscal Year
- FY2024
- FY2023
- FY2022
- FY2026
- FY2025
Start Fiscal Year
- FY2021
TIP Programs
- SBIR Phase II
Small Business
- Yes
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
Technology Foci
- Artificial Intelligence (excluding ML)
- (confidence score: 100%)
Congressional District at Award
- District n. 26 of New York
Current Congressional District
- District n. 26 of New York
United States
- New York
Core Based Statistical Area (CBSA)
- Buffalo-Cheektowaga, NY
County
- County: Erie, NY
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