Self-regulated learning with approximate reasoning and situation awareness

We present a decision support system for seamless and self-regulating learning. The decision support system presents a degree of novelty in supporting learners since it allows to: (1) understand the concepts that a learner may have acquired during her/his daily life activities, and (2) make the learner aware of these concepts and enforcing learning paths. Two key ideas are behind our results. The first idea relates to the identification of classes of indiscernible competences, and comes from the intuition that some real-world activities can lead to the acquisition of sets of competences (not always easy to discriminate) which can be considered as good approximations of competences related to a specific concept. Classes of indiscernible competences are building blocks that our decision support system uses to understand, with a certain degree of approximation, the concepts that a learner may have acquired, and this is an added value with regard to the self-awareness of a learner. The second idea is to allow our decision support system to identify incremental learning situations, which are situations in which a learning path is enforced or modified by the recognition that some concepts may have been learned, also during the execution of daily life activities. The decision support system grounds on three-way decisions and situation awareness. An evaluation of the system following the SAGAT approach has been done and reported in the paper.