The adaptability in systems of education and learning requires a detailed photograph of the student's mental state. In other words, a well-developed set of information able to diagnose with reasonable precision what the student knows and how much he knows, in order to infer what he does not know: his learning gaps. This set of information pieces regarding student knowledge and skills can be obtained through an ongoing learning assessment process that makes possible to specify, with fair accuracy, which subject the student is better suited to learn at that moment. On the other hand, this process of data collection generates a great amount of data requiring automatic or semi-automatic procedures for treatment and analysis for acquisition of new knowledge for next step. This paper presents a model for organizing and measuring knowledge upgrade in systems of education and learning with the support of data mining tools.