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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.

The importance of both social processes and of representational aids for learning is well-established, yet few experimental studies have addressed the combination of these factors. The research reported in this article evaluates the influence of tools for constructing representations of evidential models on collaborative learning processes and outcomes. Pairs of participants worked with 1 of 3 representations (Graph, Matrix, Text) while investigating complex science and public health problems. Dependent measures included (a) the content of participants' utterances and representational actions and the timing of these utterances and actions with respect to the availability of information; (b) a multiple choice test of the ability to recall the data, hypotheses, and evidential relations explored; and (c) the contents of a written essay. The results show that representational notations can have significant effects on learners' interactions, and may differ in their influence on subsequent collaborative use of the knowledge being manipulated. For example, Graph and Matrix users elaborated on previously represented information more than Text users. Representation and discussion of evidential relations was quantitatively greatest for Matrix users as predicted, yet this came at the cost of excessive consideration and revision of unimportant relations. Graph users may have been more focused in their consideration of evidence, and the work done in the Graph representation had the greatest impact on the contents of the essays. Although limited to initial use of representations in a laboratory setting, the work demonstrates that representational guidance of collaborative learning is worthy of study and suggests several lines of further investigation.

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.

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