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ANALYSIS THE KEY TOPICS AND IDENTIFICATION OF SOCIAL AND UNIVERSITY NETWORKS BASED ON PAPERS RELATED TO ARTIFICIAL INTELLIGENCE IN EDUCATION

Małgorzata PRZYBYŁA– KASPEREK1, Filip SMYCZEK2, Eugenia SMYRNOVA– TRYBULSKA3, Nataliia MORZE4, Olena KUZMINSKA5

Publication language: English

journal paper

Transformations No. 4(119)2023,  Publication date: 31 December 2023

Article No. 20231231110128557

Keywords: trends in education, hierarchical clustering algorithm, text-mining, social networks

Abstract The paper presents research on bibliometric analysis of papers related to artificial intelligence (AI) in education. The study focuses on two main objectives. Firstly, it aims to identify the key topics covered in the paper collection. To achieve this, a method is proposed that utilizes a matrix representation of paper keywords using the term frequency-inverse document frequency approach. However, due to the sparsity of the matrix, clustering algorithms alone yield unsatisfactory results. To overcome this, a novel method is introduced, which groups keywords based on character similarity. An agglomerative hierarchical clustering algorithm is then employed to group papers with similar keywords. The results reveal 2 that intelligent tutoring systems, expert systems, interactive learning environments, Chatbot, Facebook, and Messenger are the most prominent topics in AI in education in recent years. The second focus of the paper is the analysis of cooperation networks established through joint papers, considering authors, academic units, and countries. The study identifies and analyzes the scientific units, countries, and authors that exert a significant influence on these cooperation networks. Furthermore, the paper discusses the consistency of the obtained graphs in the context of international and inter-institutional collaborations. Overall, this research contributes to understanding the prevalent topics in AI in education and sheds light on the cooperation dynamics among researchers. The findings offer valuable insights for researchers, institutions, and countries interested in fostering collaborations and advancing the field of AI in education.

  1. University of Silesia in Katowice, Faculty of Science and Technology, PolandIorcid.org

    ORCID: 0000-0003-0616-9694

    E-mail: malgorzata.przybyla-kasperek@us.edu.pl

  2. University of Silesia in Katowice, Faculty of Science and Technology, Poland

    ORCID: https://orcid.org/0009-0001-1363-0007

    E-mail: fsmyczek@us.edu.pl

  3. University of Silesia in Katowice, Faculty of Arts and Educational Sciences, Poland

    ORCID: https://orcid.org/0000-0003-1227-014X

    E-mail: esmyrnova@us.edu.pl

  4. Borys Grinchenko Kyiv University, Faculty of Information Technologies and Mathematic, Ukraine

    ORCID: https://orcid.org/0000-0003-3477-9254

    E-mail: n.morze@kubg.edu.ua

  5. The National University of Life and Environmental Sciences of Ukraine, Faculty of Information Technologies, Ukraine

    ORCID: https://orcid.org/0000-0002-8849-9648

    E-mail: o.kuzminska@nubip.edu.ua