Open AccessDevelopment of knowledge testing systems based on discrete optimization models
Author(s) -
Lidia A. Zaozerskaya,
В. А. Планкова
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1441/1/012183
Subject(s) - computer science , set (abstract data type) , knowledge base , test (biology) , base (topology) , mathematical optimization , machine learning , theoretical computer science , artificial intelligence , mathematics , programming language , paleontology , mathematical analysis , biology
We present an approach to creating computer testing systems and an overview of our results obtained based on it. Special attention is paid to the problem of forming the optimal test content. This problem consists of finding a subset of the test tasks allowing us to get an objective conclusion about the degree of assimilation by a student the training course. Several formulations are described for solving this problem and the problem of forming an optimal test kit. Corresponding mathematical models are constructed on the base of the set covering problem and its generalizations. A specialized computer testing system, developed by us based on this approach, is described. The experience of this system use and the prospects of the proposed approach for creating knowledge testing systems are discussed.