  How to design automated procedures which (i) accurately assess the knowledge
of a student, and (ii) efficiently provide advices for further study? To
produce well-founded answers, Knowledge Space Theory relies on a combinatorial
viewpoint on the assessment of knowledge, and thus departs from common,
numerical evaluation. Its assessment procedures fundamentally differ from other
current ones (such as those of S.A.T. and A.C.T.). They are adaptative (taking
into account the possible correctness of previous answers from the student) and
they produce an outcome which is far more informative than a crude numerical
mark. This chapter recapitulates the main concepts underlying Knowledge Space
Theory and its special case, Learning Space Theory. We begin by describing the
combinatorial core of the theory, in the form of two basic axioms and the main
ensuing results (most of which we give without proofs). In practical
applications, learning spaces are huge combinatorial structures which may be
difficult to manage. We outline methods providing efficient and comprehensive
summaries of such large structures. We then describe the probabilistic part of
the theory, especially the Markovian type processes which are instrumental in
uncovering the knowledge states of individuals. In the guise of the ALEKS
system, which includes a teaching component, these methods have been used by
millions of students in schools and colleges, and by home schooled students. We
summarize some of the results of these applications.
