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.

We present a program visualization tool called Jeliot 3 that is designed to aid novice students to learn procedural and object oriented programming. The key feature of Jeliot is the fully or semi-automatic visualization of the data and control flows. The development process of Jeliot has been research-oriented, meaning that all the different versions have had their own research agenda rising from the design of the previous version and their empirical evaluations. In this process, the user interface and visualization has evolved to better suit the targeted audience, which in the case of Jeliot 3, is novice programmers. In this paper we explain the model for the system and introduce the features of the user interface and visualization engine. Moreover, we have developed an intermediate language that is used to decouple the interpretation of the program from its visualization. This has led to a modular design that permits both internal and external extensibility.

We present JAWAA 2.0, a scripting language for creating animations easily over the web. JAWAA includes primitives, easy creation of data structures and operations on these structures, and an editor for easy creation of complex objects. We show how to use JAWAA in a range of computer science courses including CS 0, CS 1, CS 2 and advanced courses. Instructors can quickly build animations for demos in lecture, and students can enhance their programming projects with an animation.

Creating Animation for Presentations by Douglas Zongker Chair of Supervisory Committee: Professor David H. Salesin Computer Science & Engineering In recent years the use of computer-generated slides to accompany live presentation has become increasingly common. There is a potential for using computer graphics to increase the effectiveness of this type of presentation. The hardware for generating and projecting complex scenes and animation is in place, yet few efforts have been made in creating software to fully utilize these capabilities.

Many algorithm visualizations have been created, but little is known about which features are most important to their success. We believe that pedagogically useful visualizations exhibit certain features that hold across a wide range of visualization styles and content. We began our efforts to identify these features with a review that attempted to identify an initial set of candidates. We then ran two experiments that attempted to identify the e#ectiveness for a subset of features from the list. We identified a small number of features for algorithm visualizations that seem to have a significant impact on their pedagogical e#ectiveness, and found that several others appear to have little impact. The single most important feature studied is the ability to directly control the pace of the visualization. An algorithm visualization having a minimum of distracting features, and which focuses on the logical steps of an algorithm, appears to be best for procedural understanding of the algorithm. Providing a good example for the visualization to operate on proved significantly more e#ective than letting students construct their own data sets. Finally, a pseudocode display, a series of questions to guide exploration of the algorithm, or the ability to back up within the visualization did not show a significant e#ect on learning.

Understanding data structures and algorithms, both of which are abstract concepts, is an integral part of software engineering and elementary computer science education. However, people usually have difficulty in understanding abstract concepts and processes such as procedural encoding of algorithms and data structures. One way to improve their understanding is to provide visualizations to make the abstract concepts more concrete. This thesis presents the design, implementation and evaluation for the Matrix application framework that occupies a unique niche between the following two domains. In the first domain, called algorithm animation, abstractions of the behavior of fundamental computer program operations are visualized. In the second domain, called algorithm simulation, the framework for exploring and understanding algorithms and data structures is exhibited. First, an overview and theoretical basis for the application framework is presented. Second, the different roles are defined and examined for realizing the idea of algorithm

This research began by investigating the literature on student learning from algorithm animations and conducting experimental studies of an algorithm visualization system. The results led us to develop CAROUSEL (Collaborative Algorithm Representations Of Undergraduates for Self-Enhanced Learning), using which students created expository representations of algorithms, shared their representations with others, evaluated each other's representations and discussed them. The system and the activities of representation creation, sharing, evaluation and discussion that it supports were then studied in three experiments, which are summarized. They show a significant positive relationship between these constructive and collaborative activities and algorithm learning, which suggests that this is a beneficial pedagogical approach for introductory courses on algorithms.

Engaging students in the learning process has been shown to be an effective means for education. Several methods have been proposed to achieve this engagement for computer science and other disciplines. Active learning is one such technique that incorporates interactive classroom activities to reinforce concepts and involve the students. Visualizations of computer science concepts such as algorithm animations can be used for these activities. To be most effective in this environment, they need to be designed and used with active learning in mind. This paper describes the design characteristics of such visualizations, ways of using them in the classroom, and our experience with developing and using visualization tools across different courses in the computer science curriculum.

This paper describes the design of the CIspace interactive visualization tools for teaching and learning Artificial Intelligence. Our approach to design is to iterate through three phases: identifying pedagogical and usability goals for supporting both educators and students, designing to achieve these goals, and then evaluating our system. We believe identifying these goals is essential in confronting the usability deficiencies and mixed results about the pedagogical effectiveness of interactive visualizations reported in the Education literature. The CIspace tools have been used and positively received in undergraduate and graduate classrooms at the University of British Columbia and internationally. We hope that our experiences can inform other developers of interactive visualizations and encourage their use in classrooms and other learning environments.

WWW home page:http://www.cs.joensuu.fi/˜snevalai/ Abstract. The empirical evaluation of program visualisation has been based mostly on observations of long-term effects of the program visualisation tools, while possible short-term effects of the visualisations and their relation to the long-term effects have been elided. In order to study short-term effects of visualisation of variables in a context where the long-term effects are already known, we conducted a controlled experiment, in which we investigated how a person targets her visual attention and what kind of a mental model she constructs, when variables are presented either textually or graphically. The results indicate clear differences in the targeting of visual attention between the visualisation tools: With the graphical tool, the participants targeted their visual attention to variables much more than with the textual tool. With the graphical tool, the increase of visual attention to variables increased the proportion of high-level information in program summaries and decreased the proportion of low-level code-related information. 1