Selected Proposal

Principal Investigators

Angela Kelly, Middle & High School Education, Lehman College
Jinlin Chen, Computer Science, Queens College
Serigne Gningue, Middle & High School Education, Lehman College
Rathika Rajaravivarma, Computer Engineering Technology, New York City Tech
Subash Shankar, Computer Science, Hunter College

Summary

The proposed project will synthesize and analyze recent NSF grant awards related to mathematics and science education at the City University of New York.  Researchers at CUNY have been quite successful in securing funding for such projects in recent years, and this proposal seeks to examine outcomes of past awards to highlight trends, major impacts, and potential theoretical gaps.  The primary goal of this proposed project is to create a technical report that will be widely available for the purpose of guiding future CUNY-initiated NSF submissions, with the intention that these projects will have an increased likelihood of securing funding for innovations in science and math education.  In addition, the team will develop an extensible platform for future grant applicants to retrieve information on relevant projects.

 

CUNY faculty may look towards this research to craft proposals that take existing models and further improve and broaden their impacts, and they may also utilize the proposed synthesis report to identify gaps that have not been addressed in recently funded projects.  The report will identify aspects of NSF submissions that were instrumental in their success; the results will also be shared at conferences and through peer-reviewed publications.  The project will be completed in the spring of 2009.

The methodology of the proposed synthesis project will follow a sequence of project taxonomy definition, information collection, data analysis, report generation, and result dissemination.  A project information management system (PIMS) will be developed to help manage and process project information and disseminate the results for future applications.  Data will be obtained from NSF's Fastlane system, CUNY's Office of Academic Affairs, the Research Foundation of CUNY, and through semi-structured interviews with project PIs.

Input data will be preprocessed to handle incomplete project information and improve data quality using data cleaning techniques, and data analysis will be performed at four levels:

1.     Fact Analysis : Synthesis and derivation of statistical information on grant characteristics using database technologies.

2.     Impact Analysis : Qualitative analysis of needs and broader impacts based on project goals, methodologies, implementation, evaluations, and outreach.

3.     Tactic Analysis : Identification and analysis of common project characteristics, patterns, and trends, classification of projects using various criteria, and deriving project relationships using data warehousing/data mining technologies that maintain statistical validity.

4.     Strategic Analysis : Further qualitative examination of project information to identify strengths, gaps, lessons learned, and success strategies from different project perspectives.