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.

No abstract

Many individual instructors -- and, in some cases, entire universities -- are gravitating towards the use of comprehensive learning management systems (LMSs), such as Blackboard and Moodle, for managing courses and enhancing student learning. As useful as LMSs are, they are short on features that meet certain needs specific to computer science education. On the other hand, computer science educators have developed--and continue to develop-computer-based software tools that aid in management, teaching, and/or learning in computer science courses. In this report we provide an overview of current CS specific on-line learning resources and guidance on how one might best go about extending an LMS to include such tools and resources. We refer to an LMS that is extended specifically for computer science education as a Computing Augmented Learning Management System, or CALMS. We also discuss sound pedagogical practices and some practical and technical principles for building a CALMS. However, we do not go into details of creating a plug-in for some specific LMS. Further, the report does not favor one LMS over another as the foundation for a CALMS.

The educational impact of visualization depends not only on how well students learn when they use it, but also on how widely it is used by instructors. Instructors believe that visualization helps students learn. The integration of visualization techniques in classroom instruction, however, has fallen far short of its potential. This paper considers this disconnect, identifying its cause in a failure to understand the needs of a key member in the hierarchy of stakeholders, namely the instructor. We describe these needs and offer guidelines for both the effective deployment of visualizations and the evaluation of instructor satisfaction. We then consider different forms of evaluation and the impact of student learning styles on learner outcomes.

The educational impact of visualization depends not only on how well students learn when they use it, but also on how widely it is used by instructors. Instructors believe that visualization helps students learn. The integration of visualization techniques in classroom instruction, however, has fallen far short of its potential. This paper considers this disconnect, identifying its cause in a failure to understand the needs of a key member in the hierarchy of stakeholders, namely the instructor. We describe these needs and offer guidelines for both the effective deployment of visualizations and the evaluation of instructor satisfaction. We then consider different forms of evaluation and the impact of student learning styles on learner outcomes.

Although algorithm visualizations have become numerous, they still have not been successfully adapted into mainstream computer science education. Algorithm visualization systems need to better address pedagogical requirements for effective educational use. We discuss the relevance of several such requirements that are not supported in most systems. The combination of two existing algorithm visualization systems implements these requirements and thereby provides a rich testbed for future studies of effectiveness.

No abstract