Quasi-Fractal Model of the Semantic Knowledge Network as the Basis for the Formation of a Pedagogical Test
- DOI
- 10.2991/icdee-19.2019.9How to use a DOI?
- Keywords
- subject area of knowledge, information model of knowledge representation, semantic knowledge network, graph, set of vertices and edges of a graph, fractal and quasi-fractal graphs, pedagogical test, test task
- Abstract
It is substantiated that one of the problematic issues of pedagogical testing is the inconsistency (or incomplete compliance) of the structure and content of test tasks, on the one hand, and the information model of knowledge representation about the academic discipline implemented in the learning process, on the other hand. The approach to resolving this issue is set forth where the semantic knowledge network is considered as an information model for representing knowledge about a subject area. The definition of the quasi-fractal graph is introduced, it is shown that the semantic network of knowledge about the subject area, as well as the information model of the pedagogical test, can be represented as quasi-fractal graphs. It is shown that the quasi-fractal graph is one of the forms for recording the semantic knowledge network. The conditions under which the structure and content of the test tasks will correspond to the information model of knowledge representation about the academic discipline being implemented in the learning process.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - V.I. Serdyukov AU - N.A. Serdyukova PY - 2019/05 DA - 2019/05 TI - Quasi-Fractal Model of the Semantic Knowledge Network as the Basis for the Formation of a Pedagogical Test BT - Proceedings of the International Conference on the Development of Education in Eurasia (ICDEE 2019) PB - Atlantis Press SP - 50 EP - 54 SN - 2352-5398 UR - https://doi.org/10.2991/icdee-19.2019.9 DO - 10.2991/icdee-19.2019.9 ID - Serdyukov2019/05 ER -