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Generation as method for explorative learning in computer science education

Published: 28 June 2004 Publication History

Abstract

The use of generic and generative methods for the development and application of interactive educational software is a relatively unexplored area in industry and education. Advantages of generic and generative techniques are, among other things, the high degree of reusability of systems parts and the reduction of development costs. Furthermore, generative methods can be used for the development or realization of novel learning models. In this paper, we discuss such a learning model that propagates a new way of explorative learning in computer science education with the help of generators. A realization of this model represents the educational software GANIFA on the theory of generating finite automata from regular expressions. In addition to the educational system's description, we present an evaluation of this system.

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    cover image ACM Conferences
    ITiCSE '04: Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
    June 2004
    296 pages
    ISBN:1581138369
    DOI:10.1145/1007996
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 28 June 2004

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    Author Tags

    1. animation
    2. evaluation
    3. explorative learning
    4. finite automata
    5. generation
    6. visualization

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    • (2015)A Vision of Smart Teaching in CSSmart Learning Objects for Smart Education in Computer Science10.1007/978-3-319-16913-2_1(3-32)Online publication date: 2015

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