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

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Purpose – Development of a system called Structured Hypermedia Algorithm Explanation (SHALEX), as a remedy for the limitations existing within the current traditional algorithm animation systems. SHALEX provides several novel features, such as use of invariants, reflection of the high-level structure of an algorithm rather than low-level steps, and support for programming the algorithm in any procedural or object-oriented programming language. Design/methodology/approach – By defining the structure of an algorithm as a directed graph of abstractions, algorithms may be studied top-down, bottom-up, or using a mix of the two. In addition, SHALEX includes a learner model to provide spatial links, and to support evaluations and adaptations. Findings – Evaluations of traditional algorithm animation systems designed to teach algorithms in higher education or in professional training show that such systems have not achieved many expectations of their developers. One reason for this failure is the lack of stimulating learning environments which support the learning process by providing features such as multiple levels of abstraction, support for hypermedia, and learner-adapted visualizations. SHALEX supports these environments, and in addition provides persistent storage that can be used to analyze students ’ performance. In particular, this storage can be used to represent a student model that supports adaptive system behavior. Research limitations/implications – SHALEX is being implemented and tested by the authors and a group of students. The tests performed so far have shown that SHALEX is a very useful tool. In the future we plan additional quantitative evaluation to compare SHALEX with other AA systems and/or the concept keyboard approach.