The language of pre-topology in knowledge spaces
This work addresses foundational issues in knowledge space theory, which is incremental but relevant for educational assessment and cognitive modeling.
The paper tackles the problem of characterizing skill multimaps that yield knowledge spaces in knowledge structure theory, providing a characterization for items with finitely many competencies and an algorithm to find atom primary items in finite knowledge spaces.
We systematically study some basic properties of the theory of pre-topological spaces, such as, pre-base, subspace, axioms of separation, connectedness, etc. Pre-topology is also known as knowledge space in the theory of knowledge structures. We discuss the language of axioms of separation of pre-topology in the theory of knowledge spaces, the relation of Alexandroff spaces and quasi ordinal spaces, and the applications of the density of pre-topological spaces in primary items for knowledge spaces. In particular, we give a characterization of a skill multimap such that the delineate knowledge structure is a knowledge space, which gives an answer to a problem in \cite{falmagne2011learning} or \cite{XGLJ} whenever each item with finitely many competencies; moreover, we give an algorithm to find the set of atom primary items for any finite knowledge spaces.