Compressed representation of Learning Spaces
Data Structures and Algorithms
2017-08-14 v4
Abstract
Learning Spaces are certain set systems that are applied in the mathematical modeling of education. We propose a suitable 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.
Cite
@article{arxiv.1407.6327,
title = {Compressed representation of Learning Spaces},
author = {Marcel Wild},
journal= {arXiv preprint arXiv:1407.6327},
year = {2017}
}
Comments
33 pages, 8 figures, 11 Tables. Section 8 on query learning is thoroughly revised