Logical-semantic knowledge model for the knowledge base of a lecturer

The subject of this study is the process of synthesizing a graphical logical-semantic knowledge representation model with an open architecture for the knowledge base of a university lecturer. The purpose of the proposed model is to develop a lecturer’s knowledge base using a graphical logical-semantic knowledge representation model. The objectives of this study include: analysis of existing approaches to the formation of information management systems for organizing the educational process in higher education institutions; definition of the research methodology; analysis of the integrated four-factor architecture of the knowledge model about the existence and activities of the socio-economic ecosystem; formation of a three-factor logical-semantic knowledge representation model based on the results of the analysis; presentation of the practical implementation of the three-factor logical-semantic knowledge representation model for the lecturer’s knowledge base; and summarizing the research results and outlining future directions in the field of knowledge modeling for knowledge bases with open architecture. The methods used include an approach that identifies general patterns and hypotheses underlying the construction of lecturers’ knowledge bases to organize the educational process in higher education institutions. The theoretical foundation proposes using a logical-semantic model of semiotics knowledge because all known logical and logical-semantic knowledge models are representative objects of semiotics. The following results were obtained. The current focus of implementing information technologies in educational activities is on organizing educational processes in a distance format. At the same time, an important aspect of lecturers’ work—providing information supports for their methodological activities—has been overlooked. This work involves preparing initial materials, processing them, and forming the corresponding final materials in the form of textbooks, educational aids, lecture notes, methodological guidelines, and presentations. This paper proposes an architecture for a three-factor graphical logical-semantic knowledge representation model, which defines an algorithm to form the corresponding knowledge base. According to the authors, this knowledge base can be best implemented in Microsoft Excel. The advantage of this knowledge base model is its open architecture, as users hold administrator rights over the knowledge base. The user makes decisions about including relevant knowledge elements in the knowledge base. The inclusion of the "Students" factor in the knowledge base and subsequent recording of their participation results in classes ensures the possibility of providing these results to department and faculty management for further analysis. Conclusions. The architecture of the graphical logical-semantic knowledge representation model and its corresponding knowledge base ensures the resolution of tasks related to the preparation of methodological support for courses. It can also complement existing LMS (Learning Management Systems) and LCMS (Learning Content Management Systems). The next step in using the formed knowledge bases for relevant educational components (courses) is to create department knowledge based on these knowledge bases. This ensures the formation and preservation of the department's intellectual potential and transferability.

Authors

Institutions

Publication Details

Journal
RADIOELECTRONIC AND COMPUTER SYSTEMS
Published
2024-11-21
DOI
https://doi.org/10.32620/reks.2024.4.18
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Logical-semantic knowledge model for the knowledge base of a lecturer

Serhii Dotsenko, Olga Morozova, Ольга Баніт, Dmytro Nor
RADIOELECTRONIC AND COMPUTER SYSTEMS
Intelligent Tutoring Systems and Adaptive Learning
article

Logical-semantic knowledge model for the knowledge base of a lecturer

Serhii Dotsenko, Olga Morozova, Ольга Баніт, Dmytro Nor
article en

Abstract

The subject of this study is the process of synthesizing a graphical logical-semantic knowledge representation model with an open architecture for the knowledge base of a university lecturer. The purpose of the proposed model is to develop a lecturer’s knowledge base using a graphical logical-semantic knowledge representation model. The objectives of this study include: analysis of existing approaches to the formation of information management systems for organizing the educational process in higher education institutions; definition of the research methodology; analysis of the integrated four-factor architecture of the knowledge model about the existence and activities of the socio-economic ecosystem; formation of a three-factor logical-semantic knowledge representation model based on the results of the analysis; presentation of the practical implementation of the three-factor logical-semantic knowledge representation model for the lecturer’s knowledge base; and summarizing the research results and outlining future directions in the field of knowledge modeling for knowledge bases with open architecture. The methods used include an approach that identifies general patterns and hypotheses underlying the construction of lecturers’ knowledge bases to organize the educational process in higher education institutions. The theoretical foundation proposes using a logical-semantic model of semiotics knowledge because all known logical and logical-semantic knowledge models are representative objects of semiotics. The following results were obtained. The current focus of implementing information technologies in educational activities is on organizing educational processes in a distance format. At the same time, an important aspect of lecturers’ work—providing information supports for their methodological activities—has been overlooked. This work involves preparing initial materials, processing them, and forming the corresponding final materials in the form of textbooks, educational aids, lecture notes, methodological guidelines, and presentations. This paper proposes an architecture for a three-factor graphical logical-semantic knowledge representation model, which defines an algorithm to form the corresponding knowledge base. According to the authors, this knowledge base can be best implemented in Microsoft Excel. The advantage of this knowledge base model is its open architecture, as users hold administrator rights over the knowledge base. The user makes decisions about including relevant knowledge elements in the knowledge base. The inclusion of the "Students" factor in the knowledge base and subsequent recording of their participation results in classes ensures the possibility of providing these results to department and faculty management for further analysis. Conclusions. The architecture of the graphical logical-semantic knowledge representation model and its corresponding knowledge base ensures the resolution of tasks related to the preparation of methodological support for courses. It can also complement existing LMS (Learning Management Systems) and LCMS (Learning Content Management Systems). The next step in using the formed knowledge bases for relevant educational components (courses) is to create department knowledge based on these knowledge bases. This ensures the formation and preservation of the department's intellectual potential and transferability.

RADIOELECTRONIC AND COMPUTER SYSTEMSVol. 2024(4)
National Aerospace University – Kharkiv Aviation Institute (UA), Ukrainian State University of Railway Transport (UA), National Academy of Educational Sciences of Ukraine (UA)
Openalex Percentile: Top 77%
Intelligent Tutoring Systems and Adaptive Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.