No abstract

This paper proposes a collaborative approach, which combines two processes: the extraction of approximate information from a rough relational database and its generalization into symbolical rules. A rough relational database model is basically a standard relational database model extended with some of the essential features of the rough set theory. The rough approach to relational databases allows the user to represent imprecision in querying, which gives a greater flexibility to the querying and also, improves the representational power of a relational database. The paper describes the prototype system ROUGH-ID3, which implements a hybrid knowledge extraction approach by integrating a set of rough database operators with the symbolic system ID3.

Learning trajectories are paths that students may follow in order to achieve learning goals. The visualization of learning trajectories of students can support teachers in tracking students evolution and identify difficulties. We propose visualizations of learning trajectories in a new and interactive way, representing different concepts of computational thinking and learning goals in concise or detailed manner, according to interactions of the user. To evaluate our proposal, we chose to represent a series of exercises found in code.org, a free and well known platform that introduces and exercises computational thinking through visual programming. These visualizations were evaluated by 20 elementary school teachers in usability perspective.