For seventeen years the BioQUEST Curriculum Consortium has strived to place powerful, professional tools into the hands of students as well as empowering them through active participation in problem posing, problem solving, peer review, and publication. How will we embrace new opportunities that exist for collaboration, communication, computation, modeling, simulation, visualization, and data mining? How will these new technologies enable us to further the long-term BioQUEST goals of broad access, equity, and cross-disciplinary linkages? Clearly, the technical barriers to accessing rich information, running analyses on supercomputers, and communicating broadly are constantly being lowered. How can we take educational advantage of these opportunities and tools to help us bridge the social, intellectual, national, and cultural barriers and connect more deeply with learners?

With creativity, we can broaden our vision of how to utilize emerging technologies (networking, computational power, informatics). Technology-enabled changes in undergraduate science education need not merely reflect an emphasis on doing more efficiently what we already do by creating electronic versions of old material. BioQUEST has tried to move curriculum development from the consideration of helping us teach more efficiently to helping students learn more effectively. Now, we need to think about breaking down the barriers between individual campuses, classrooms and laboratories; how will linking learners, data, tools, and analyses transform the student 's learning landscape?

We encourage applicants to join us in developing white papers on establishing a variety of new agendas for biology education reform. First, collaboratories have made it possible for investigators to have access to high-end scientific equipment over vast regions of spaces and at odd times; how do we build on or transform access to these tools to suit the needs of educators and students? Or is a distinctly different kind of collaboratory needed for science education that employs the electronic notebooks, communication, and sharing potential of other collaboratories? Second, with the major strides being made to establish national science, mathematics, engineering, and technology educational digital libraries, how will we utilize these resources? Third, with the explosion of applications under the umbrella of computational biology, we are moving from period of minimal professional software and primarily toy data sets being available to students to a huge abundance of complex choices. How will we develop criteria for choosing appropriate software, hardware, and problem environments to fully serve student scientific exploration?