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Computer science education should not be based on short-term developments but on content that is observable in multiple domains of computer science, may be taught at every intellectual level, will be relevant in the longer term, and is related to everyday language and/or thinking. Recently, a catalogue of central concepts for computer science education such as algorithm, computer, and data has been empirically determined. This paper now concentrates on central processes relevant for computer science education. Experts of computer science rated 44 general-education processes regarding four criteria. By using a cluster analysis approach the following central processes for computer science education have been identified: problem solving and problem posing, analyzing, generalizing, finding relationships, classifying, and investigating. [ABSTRACT FROM AUTHOR]

Educators in the computing fields are often familiar with the characterization of computing as a combination of theoretical, scientific, and engineering traditions. That distinction is often used to guide the work and disciplinary self-identity of computing professionals. But the distinction is, by no means, an easy one. The three traditions of computing are based on different principles, they have different aims, they employ different methods, and their products are very different. Educators in the field of computing should be aware of the fundamental differences between the traditions of computing so that they can offer their students a truthful and balanced view about computing branches. In this article the three traditions of computing are presented and some of their underlying assumptions, principles, application areas, restrictions, and weaknesses are portrayed. Also, some of the landmark arguments in the debates about the identity of computing disciplines are discussed. [ABSTRACT FROM AUTHOR]