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Learning how to use a new software program can be a difficult and demanding task, especially for novices. There are several types of support for users exploring a software package. Animated demonstrations show how experts use an application, and training wheels interfaces offer a secure environment for exploration. To support different types of learners, external help should be adapted according to learner characteristics. The study presented in this article investigates effects of different support types in combination with the computer self-efficacy of learners. Young students (8th graders) were supported with text manuals, animated demonstrations, or animated demonstrations combined with a training wheels interface. In this context, they had to solve problems in physics and mathematics with a spreadsheet program. Results showed that animated demonstrations outperformed text manuals in many cases. Training wheels interfaces seemed to have disadvantages compared to unmodified user interfaces. In addition, motivational aspects have been investigated. Subjects with high computer self-efficacy scores were more motivated than their counterparts. Statistics (analysis of variance) revealed no interaction effects between the treatment and computer self-efficacy.

The design of computer science curricula should rely on central concepts of the discipline rather than on technical short-term developments. Several authors have proposed lists of basic concepts or fundamental ideas in the past. However, these catalogs were based on subjective decisions without any empirical support. This article describes the empirical determination of central concepts for computer science education. Experts of computer science rated 49 concepts regarding four criteria. The cluster analysis of the data revealed the following central concepts: problem, data, computer, test, algorithm, process, system, information, language, communication, software, program, computation, structure , and model .

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.

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