2 research outputs found

    A framework proposal for algorithm animation systems

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    The learning and analysis of algorithms and algorithm concepts are challenging to students due to the abstract and conceptual nature of algorithms. Algorithm animation is a form of technological support tool which encourages algorithm comprehension by visualising algorithms in execution. Algorithm animation can potentially be utilised to support students while learning algorithms. Despite widespread acknowledgement for the usefulness of algorithm animation in algorithm courses at tertiary institutions, no recognised framework exists upon which algorithm animation systems can be effectively modelled. This dissertation consequently focuses on the design of an extensible algorithm animation framework to support the generation of interactive algorithm animations. A literature and extant system review forms the basis for the framework design process. The result of the review is a list of requirements for a pedagogically effective algorithm animation system. The proposed framework supports the pedagogic requirements by utilising an independent layer structure to support the generation and display of algorithm animations. The effectiveness of the framework is evaluated through the implementation of a prototype algorithm animation system using sorting algorithms as a case study. This dissertation is successful in proposing a framework to support the development of algorithm animations. The prototype developed will enable the integration of algorithm animations into the Nelson Mandela Metropolitan University’s teaching model, thereby permitting the university to conduct future research relating to the usefulness of algorithm animation in algorithm courses

    Animating Algorithms Live and Post Mortem

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    We first give an overview of the features of the GANIMAL Framework introducing several new concepts not present in any previous algorithm animation system. Then we focus on its mechanisms for mixing live and post mortem visualization which are in particular very useful for algorithms which restructure graphs.
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