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Knowledge Spaces and Learning Spaces
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
On the completeness of the universal knowledge-belief space
Meier (2008) shows that the universal knowledge-belief space exists. However, besides the universality there is an other important property might be imposed on knowledge-belief spaces, inherited also from type spaces, the completeness. In this paper we introduce the notion of complete knowledge-belief space, and demonstrate that the universal knowledge-belief space is not complete, that is, some subjective beliefs (probability measures) on the universal knowledge-belief space are not knowledge-belief types
Knowledge Spaces and the Completeness of Learning Strategies
We propose a theory of learning aimed to formalize some ideas underlying
Coquand's game semantics and Krivine's realizability of classical logic. We
introduce a notion of knowledge state together with a new topology, capturing
finite positive and negative information that guides a learning strategy. We
use a leading example to illustrate how non-constructive proofs lead to
continuous and effective learning strategies over knowledge spaces, and prove
that our learning semantics is sound and complete w.r.t. classical truth, as it
is the case for Coquand's and Krivine's approaches
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