Visualization technology can be used to graphically illustrate various concepts in computer science. We argue that such technology, no matter how well it is designed, is of little educational value unless it engages learners in an active learning activity. Drawing on a review of experimental studies of visualization effectiveness, we motivate this position against the backdrop of current attitudes and best practices with respect to visualization use. We suggest a new taxonomy of learner engagement with visualization technology. Grounded in Bloom's wellrecognized taxonomy of understanding, we suggest metrics for assessing the learning outcomes to which such engagement may lead. Based on these taxonomies of engagement and effectiveness metrics, we present a framework for experimental studies of visualization effectiveness. Interested computer science educators are invited to collaborate with us by carrying out studies within this framework.

Visualization technology can be used to graphically illustrate various concepts in computer science. We argue that such technology, no matter how well it is designed, is of little educational value unless it engages learners in an active learning activity. Drawing on a review of experimental studies of visualization effectiveness, we motivate this position against the backdrop of current attitudes and best practices with respect to visualization use. We suggest a new taxonomy of learner engagement with visualization technology. Grounded in Bloom's wellrecognized taxonomy of understanding, we suggest metrics for assessing the learning outcomes to which such engagement may lead. Based on these taxonomies of engagement and effectiveness metrics, we present a framework for experimental studies of visualization effectiveness. Interested computer science educators are invited to collaborate with us by carrying out studies within this framework.

N-ethyl-N-nitrosourea (ENU) introduces mutations throughout the mouse genome at relatively high efficiency. Successful high-throughput phenotype screens have been reported and alternative screens using sequence-based approaches have been proposed. For the purpose of generating an allelic series in selected genes by a sequence-based approach, we have constructed an archive of over 4000 DNA samples from individual F1 ENU-mutagenized mice paralleled by frozen sperm samples. Together with our previously reported archive, the total size now exceeds 6000 individuals. A gene-based screen of 27.4 Mbp of DNA, carried out using denaturing high-performance liquid chromatography (DHPLC), found a mutation rate of 1 in 1.01 Mbp of which 1 in 1.82 Mbp were potentially functional. Screening of whole or selected regions of genes on subsets of the archive has allowed us to identify 15 new alleles from 9 genes out of 15 tested. This is a powerful adjunct to conventional mutagenesis strategies and has the advantage of generating a variety of alleles with potentially different phenotypic outcomes that facilitate the investigation of gene function. It is now available to academic collaborators as a community resource.

JAWAA is a simple command language for creating animations of data structures and displaying them with a Web browser. Commands are stored in a script file that is retrieved and run by the JAWAA applet when the applet's Web page is accessed through the Web. JAWAA commands allow for creation and movement of primitive objects (circles, lines, text, rectangles) and data structure objects (arrays, stacks, queues, lists, trees and graphs). A JAWAA script can be generated as the output of a program written in any language.