Subject: NSF From: Norman Stahl <[log in to unmask]> Reply-To: Open Forum for Learning Assistance Professionals <[log in to unmask]> Date: Mon, 2 Apr 2012 19:05:12 -0400 Content-Type: text/plain Parts/Attachments: text/plain (52 lines) This is a notice about an upcoming NSF solicitation that you may find of interest. The NSF "Dear Colleague Letter" is included below and also available at the following links.   Dear Colleague Letter -Data-Intensive Education-Related Research Funding Opportunities Available Formats: HTML: http://www.nsf.gov/pubs/2012/nsf12060/nsf12060.jsp?WT.mc_id=USNSF_25&WT.mc_ev=click PDF: http://www.nsf.gov/pubs/2012/nsf12060/nsf12060.pdf?WT.mc_id=USNSF_25&WT.mc_ev=click TXT: http://www.nsf.gov/pubs/2012/nsf12060/nsf12060.txt?WT.mc_id=USNSF_25&WT.mc_ev=click   NSF 12-060 Dear Colleague Letter -Data-Intensive Education-Related Research Funding Opportunities DATE: March 29, 2012 The purpose of this letter is to inform you of an upcoming solicitation related to data-intensive education research that is expected to occur during FY 2012-2013 and to highlight existing complementary data-intensive education research funding opportunities. The need for transformative advances in teaching and student learning environments represents a significant challenge that requires novel ideas and innovative approaches. Today's technological capabilities to mine large datasets provide new avenues that can be valuable for developing new models of teaching and learning at the K-16 levels and beyond. The increasing availability of large datasets and the capabilities to capture additional datasets have great potential for advancing teaching and learning effectiveness in many areas. These include, for example: improving student learning and engagement; optimizing personalized instruction; and supporting adaptive, rapid decision-making. The challenge is maximizing the benefits that can be gained from analysis of the data in these large datasets. To help address this challenge, NSF expects to announce a solicitation that will call for participants for an Ideas Lab on the topic of advancing teaching and learning focused on transforming large datasets into knowledge that leads to actions that can improve learning environments. An Ideas Lab is an intensive, interactive workshop aimed to develop bold approaches to address grand challenges that could benefit from a new dimension in thinking. During the Ideas Lab, participants will work in teams to develop collaborative research proposals that will leverage existing research and develop new research directions. The participants of the Lab will be selected to ensure that they cover a range of disciplines and backgrounds to foster multidisciplinary approaches. Expected outcomes from the Lab will be research project concepts that vary in scale and scope in addressing the challenges. Funding will be available to support some or all of the meritorious research projects emerging from the Lab; however, no one will be guaranteed funding by virtue of participating in the Lab. Complementary data-intensive research funding opportunities currently exist through two programs: Building Community and Capacity for Data-Intensive Research in the Social, Behavioral, and Economic Sciences and in Education and Human Resources (NSF 12-538) and Cyberlearning: Transforming Education (NSF 11-587). For additional information, investigators are referred to the solicitations for these two programs, which can be found on the NSF website at www.nsf.gov. Sincerely, Joan Ferrini-Mundy NSF Assistant Director for Education and Human Resources Myron Gutmann NSF Assistant Director for Social, Behavioral & Economic Sciences Farnam Jahanian NSF Assistant Director for Computer & Information Science & Engineering Alan Blatecky Director of NSF Office of Cyberinfrastructure Norman Stahl [log in to unmask] ~~~~~~~~~~~~~~~ To access the LRNASST-L archives or User Guide, or to change your subscription options (including subscribe/unsubscribe), point your web browser to http://www.lists.ufl.edu/archives/lrnasst-l.html To contact the LRNASST-L owner, email [log in to unmask]

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