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Learning by generation in computer science education

Kerren, Andreas · SEDICI UNLP · 2004

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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. Facultad de Informática

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APA 7

Kerren, A. (2004). Learning by generation in computer science education. http://sedici.unlp.edu.ar/handle/10915/9485

MLA

Kerren, Andreas. "Learning by generation in computer science education." 2004. http://sedici.unlp.edu.ar/handle/10915/9485.

Chicago

Kerren, Andreas. 2004. "Learning by generation in computer science education.". http://sedici.unlp.edu.ar/handle/10915/9485.

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Kerren, A. 2004, Learning by generation in computer science education, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/9485 [Accessed 7 Aug. 2026].

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Title
Learning by generation in computer science education
Author / contributors
Kerren, Andreas
Publisher
SEDICI UNLP
Publication year
2004
Language
English

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