Computer Science (CS) Education research, specifically when focusing on secondary education, faces the difficulty of regionally differing political, legal, or curricular constraints. To date, many different studies exist that document the specific regional situations of teaching CS in secondary schools. This ITiCSE working group report documents the process of collecting, evaluating, and integrating research findings about CS in secondary schools from different countries. As an outcome, it presents a category system (Darmstadt Model), as a first step towards a framework that supports future research activities in this field and that supports the transfer of results between researchers and teachers in CS education (CSE) across regional or national boundaries. Exemplary application of the Darmstadt model shows in several important categories how different the situation of CSE in secondary education in various countries can be. The Darmstadt Model (DM) is now ready for discussion and suggestions for improvement by the CSE-community.
Fuzzy classification systems (FCS) are traditionally built from observations (data points) in an off-line one shot-experiment. Once the learning phase is exhausted, the classifier is no more capable to learn further knowledge from new observations nor is it able to update itself in the future. This paper investigates the problem of incremental learning in the context of FCS. It shows how, in contrast to off-line or batch learning, incremental learning infers knowledge in the form of fuzzy rules from data that evolves over time. To accommodate incremental learning, appropriate mechanisms are applied in all steps of the FCS construction: (1) Incremental supervised clustering to generate granules in a progressive manner, (2) Systematic and automatic update of fuzzy partitions, (3) Incremental feature selection using an incremental version of Fisher's interclass separability criterion. The effect of incrementality on various aspects is demonstrated via a numerical evaluation.
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