Compressed representation of Learning Spaces

Abstract

Learning Spaces are certain set systems that are applied in the mathematical modeling of education. We propose a wildcard-based compression (without loss of information) of such set systems to facilitate their logical and statistical analysis. Under certain circumstances compression is the prerequisite to calculate the Learning Space in the first place. There are connections to the dual framework of Formal Concept Analysis and in particular to so called attribute exploration.