What Education Funding Covers (and Excludes)
GrantID: 55660
Grant Funding Amount Low: $10,000,000
Deadline: September 6, 2023
Grant Amount High: $10,000,000
Summary
Explore related grant categories to find additional funding opportunities aligned with this program:
Education grants, Higher Education grants, Individual grants, Literacy & Libraries grants, Research & Evaluation grants, Science, Technology Research & Development grants.
Grant Overview
Scope of Data-Driven Tools for Student Success
Data-driven tools for student success in education encompass innovative technologies that leverage data analytics to enhance student learning outcomes. This funding specifically supports the development of predictive analytics systems, student performance assessment tools, and other educational software that improves academic engagement. It excludes direct interventions like tutoring or classroom management strategies that do not involve analytical data processing.
Real-World Use Cases
One example of a successful implementation of data-driven tools is the deployment of a predictive analytics platform used in a mid-sized school district. Educators utilized the system to analyze real-time attendance and assessment data, identifying students at risk of dropping out and providing targeted interventions. Similarly, a pilot program in a major urban high school successfully integrated data analytics tools to track student performance across diverse subjects, enabling timely support and resources for struggling learners.
Eligibility and Target Audience
Ideal applicants for this funding include educational institutions, technology startups focused on educational software, and research organizations working on innovative analytics solutions. Organizations not eligible are those solely interested in providing traditional educational methods without integrating data-driven insights or those who offer support services like counseling that don't involve analytics.
Factors for Successful Alignment
For applicants, alignment with this funding requires demonstrating a clear understanding of data privacy and compliance with regulations such as FERPA. Furthermore, the proposed tools should fit within existing educational frameworks, showing how they would enhance or supplement current systems rather than replace them outright. Successful applications would also portray a well-defined implementation plan that includes stakeholder engagement and technical support for schools.
Conclusion
In summary, data-driven tools enhance the educational landscape by providing valuable insights into student learning patterns. This funding represents an opportunity for innovators in education technology to create impactful solutions that directly address the needs of students and educators alike, fostering a more effective and targeted learning environment.
Eligible Regions
Interests
Eligible Requirements
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