Advances in graphical technology have now made it possible for us to interact with information in innovative ways, most notably by exploring multimedia environments and by manipulating three-dimensional virtual worlds. Many benefits have been claimed for this new kind of interactivity, a general assumption being that learning and cognitive processing are facilitated. We point out, however, that little is known about the cognitive value of any graphical representations, be they good old-fashioned (e.g. diagrams) or more advanced (e.g. animations, multimedia, virtual reality). In our paper, we critique the disparate literature on graphical representations, focusing on four representative studies. Our analysis reveals a fragmented and poorly understood account of how graphical representations work, exposing a number of assumptions and fallacies. As an alternative we propose a new agenda for graphical representation research. This builds on the nascent theoretical approach within cognitive science that analyses the role played by external representations in relation to internal mental ones. We outline some of the central properties of this relationship that are necessary for the processing of graphical representations. Finally, we consider how this analysis can inform the selection and design of both traditional and advanced forms of graphical technology.

Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it. KEY WORDS: cognition; instructional design; learning; problem solving.

Algorithm visualization (AV) technology graphically illustrates how algorithms work. Despite the intuitive appeal of the technology, it has failed to catch on in mainstream computer science education. Some have attributed this failure to the mixed results of experimental studies designed to substantiate AV technology's educational effectiveness. However, while several integrative reviews of AV technology have appeared, none has focused specifically on the software's effectiveness by analyzing this body of experimental studies as a whole. In order to better understand the effectiveness of AV technology, we present a systematic metastudy of 24 experimental studies. We pursue two separate analyses: an analysis of independent variables, in which we tie each study to a particular guiding learning theory in an attempt to determine which guiding theory has had the most predictive success; and an analysis of dependent variables, which enables us to determine which measurement techniques have been most sensitive to the learning benefits of AV technology. Our most significant finding is that how students use AV technology has a greater impact on effectiveness than what AV technology shows them. Based on our findings, we formulate an agenda for future research into AV effectiveness.

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.

A number of prior studies have found that using animation to help teach algorithms had less beneficial effects on learning than hoped. Those results surprise many computer science instructors whose intuition leads them to believe that algorithm animations should assist instruction. This article reports on a study in which animation is utilized in more of a "homework" learning scenario rather than a "final exam" scenario. Our focus is on understanding how learners will utilize animation and other instructional materials in trying to understand a new algorithm, and on gaining insight into how animations can fit into successful learning strategies. The study indicates that students use sophisticated combinations of instructional materials in learning scenarios. In particular, the presence of algorithm animations seems to make a challenging algorithm more accessible and less intimidating, thus leading to enhanced student interaction with the materials and facilitating learning. Keywords:...

Abstract not found

Two experiments examined the general claim that animations can help students learn algorithms more effectively. Animations and instructions that explicitly required learners to predict the behavior of an algorithm were used during training. Post-test problems were designed to measure how well learners could predict algorithm behavior in new situations as well as measure learners' conceptual understanding of the algorithms. In Experiment 1, we found that when learners both viewed an animation and made predictions their performance on novel problems improved compared to a control group's, but the effects of the two manipulations were not distinguishable. In Experiment 2, no effect was found for conceptual measures of learning, but a marginally reliable effect similar to the one seen in Experiment 1 was found for procedural problems. The results from the two experiments suggest that the benefits of animations are not obvious and that in order to determine whether animations can truly aid ...

Phishing attacks, in which criminals lure Internet users to websites that impersonate legitimate sites, are occurring with increasing frequency and are causing considerable harm to victims. In this paper we describe the design and evaluation of an embedded training email system that teaches people about phishing during their normal use of email. We conducted lab experiments contrasting the effectiveness of standard security notices about phishing with two embedded training designs we developed. We found that embedded training works better than the current practice of sending security notices. We also derived sound design principles for embedded training systems. Author Keywords Embedded training, phishing, email, usable privacy and

Because narrative plays such a central role in cognition and culture, narrative-centered curricula have been the subject of increasing attention. By taking advantage of the inherent structure of narrative, narrative-centered learning environments could provide engaging worlds in which students are actively involved in motivating storybuilding activities. The fundamental hypothesis of this research program is that by enabling learners to be co-constructors of narratives, narrative-centered learning environments can promote the deep, connection-building meaning-making activities that define constructivist learning. We outline the features of narrative that support constructivist learning, explore the key issues in introducing narrative into learning environments, consider how these environments can support one particular subject matter, literacy education, and sketch the research agenda required to make narrative-centered learning environments a reality.

................................................................................................3 Introduction ...........................................................................................4 Learners ............................................................................................4 Prior Knowledge ...............................................................................4 Aptitude..........................................................................................4 Materials ............................................................................................5 Spatial Information.............................................................................5 Verbal Information .............................................................................5 Tasks................................................................................................6 Information Processing..........................................................................