Cognitive Modeling and Cognitive Map Applying to the Knowledge Management in the Higher Education System

Kobets, Nataliya; Kovaliuk, Tetiana; Mozoliova, Daryna

doi:10.1007/978-3-030-30443-0_6

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…At last, the trend of higher vocational classroom research was proposed through the centrality analysis and multi-dimensional scale analysis Atlas of SPSS. Kobets et al [22] used the cognitive modeling methods of weakly structured systems to construct cognitive maps of educational processes, demonstrated the possibility of using cognitive modeling and cognitive maps in college knowledge management systems and identified target risk factors, basic risk factors and risk factors affecting the quality of educational processes in higher education systems. In order to reveal the research characteristics and status quo of big data in education, Jiang et al [23] used the software CiteSpace V to analyze 2052 papers on the national knowledge infrastructures in China.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Wu¹,

Jia²

2022

Int. J. Emerg. Technol. Learn.

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The main problems lying in the learning process of learners in different majors are that they study blindly and do not have a complex knowledge structure, which are seriously affecting their learning effects. But if a knowledge graph can be modeled for the major-specific curriculum system using the quantitative method from the perspective of knowledge network, it may be able to improve the existing teaching problems and optimize the teaching quality. The existing major-specific knowledge graphs were all constructed in an abstract form, ignoring the inherent prior learning relationship between teaching units and curriculum knowledge. To this end, taking English major as an example, this paper studied the construction and application of a major-specific knowledge graph based on the big data in education. Firstly, the English major-specific knowledge graph was modeled, the calculation process of node importance was shown, and a localized graph of the knowledge network of English major courses was given. Then, a multi-node feature selection framework for the English major-specific knowledge graph was constructed based on the context of nodes, and the importance of the top k nodes in the constructed knowledge graph was extracted using the multi-node feature extraction technology. After that, the experimental results verified the stability and connectivity of the nodes in the constructed knowledge graph.

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Section: Introductionmentioning

confidence: 99%

Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Wu¹,

Jia²

2022

Int. J. Emerg. Technol. Learn.

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“…Kobets et al [21] analyzed and summarized the problems existing in higher education in the era of knowledge economy, and used the cognitive modeling method for weakly structured systems to construct a cognitive map of the educational process. The goal was to demonstrate the possibility of using cognitive modeling and cognitive maps in university knowledge management systems.…”

Section: Introductionmentioning

confidence: 99%

Construction of the Interactive Educational Knowledge Graph and Classification of Student Groups

Sun¹

2022

Int. J. Emerg. Technol. Learn.

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The attributes of the knowledge nodes in the interactive educational knowledge graph need to cater to students’ online learning preferences, so understanding the composition and learning preferences of students in the online learning process is helpful to the development of more targeted learning paths. Currently, there are few existing research results on knowledge graph embedding methods based on students’ interaction with respect to knowledge points, student group composition and their learning preferences. To this end, this paper studies the construction of an interactive educational knowledge graph and the classification of student groups. First, a knowledge recommendation idea was proposed based on the classification of student groups. Through the three types of interaction behaviors - human-computer, teacher-student, and student-student interactions that occur on the online learning platform, the depth of students’ interactions with respect to the knowledge points in the interactive educational knowledge graph was characterized. The online learning effects of students were quantified by the interactive achievement of knowledge points mastered by students and the weights of knowledge points which represent their importance. Then, the effects of the differences in the interactions of students with respect to different knowledge points on the stability of the similarity prediction of students’ learning preferences were explored, and based on the analysis results, students were classified into groups. The experimental results verified the effectiveness of the proposed model.

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A Complex Cognitive-Based Technique for Social Tension Detection in the Internet

Korovin

Pavlenko

Klimenko

et al. 2021

Artificial Intelligence in Intelligent Systems

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Cognitive Modeling and Cognitive Map Applying to the Knowledge Management in the Higher Education System

Cited by 3 publications

References 4 publications

Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Construction of the Interactive Educational Knowledge Graph and Classification of Student Groups

A Complex Cognitive-Based Technique for Social Tension Detection in the Internet

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