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"The Predictive Analytics Reporting (PAR) Framework is a multi-institutional data mining project that brings together 2 year, 4 year, public, proprietary, traditional, and progressive institutions to collaborate on identifying points of student loss and to find effective practices that improve student retention in U.S. higher education. Current efforts focus on removing barriers to student success in online and blended programs. With sixteen WCET member institutions, over 1,700,000 anonymized student records and 8,100,000 institutionally de-identified course level records, the PAR Framework offers educational stakeholders a unique multi-instituional lens for examining dimensions of student success from both unified and contextual perspectives. More benefits of PAR Framework participation."

Education Applications (EA) Proposal Due Date: Decemeber 03, 2012 Please all inquiries about this topic to Glenn Larsen (glarsen@nsf.gov) Administrative Information The required 400-word project summary should discuss the intellectual merit and broader impact in two separate ~200 word paragraphs that specifically answer the following questions: Paragraph 1) Intellectual merit: What is the problem to be solved? How will the problem be solved? What is the specific innovation in the proposed approach? Paragraph 2) Broader impacts: Why is your solution better than competitive technologies? Who is going to buy your solution? Who are the other key players? Tools that build real-time information from data-mining on complexity, diversity, and similar types of information to generate knowledge that can be used to revise curricula, teaching, and assessment such as in learning analytics. Gesture-based computing applications that enable collaborative work with multiple students interacting on content simultaneously. Education tools that benefit from objects having their own IP address or location based services for new types of communications, assistive technologies, and new applications of benefit primarily to education.