University of Education Ludwigsburg, Germany (firstname.lastname@example.org)
University of Education Ludwigsburg, Germany (email@example.com)
University of Education Heidelberg, Germany (firstname.lastname@example.org)
University of Education Ludwigsburg, Germany (email@example.com)
A major task in planning the computer science curriculum is the specification of teaching and learning contents. This work needs to be based on knowledge of the content and process concepts central to the discipline of computer science. These central concepts are applicable or observable in multiple domains of computer science, can be taught on every intellectual level, will be relevant in the longer term, and are related to everyday language and/or thinking. Two empirically based catalogues of central content concepts (e.g., algorithm, system, process) and central process concepts (e.g., problem solving and problem posing, analyzing, classifying) for computer science education have recently been proposed. This article uses discriminant analysis techniques to provide a semantic categorization of both the content and the process concepts. On this basis, conclusions can be drawn about how individual groups of content and process concepts differ semantically.
Keywords: computer science education; central content concepts; central process concepts