Text Mining-Based Scientometric Analysis in Educational Research


This paper presents a complete scientometric analysis of a well-selected educational research journal, Hungarian Pedagogy, the most significant and the oldest Hungarian educational research journal, founded in 1892 and still being issued today. All journal articles (N=6574) have been digitised in order to build a well-structured database in our research project, which makes it possible to analyse them by means of various metadata. Besides analysing metadata, our aim is to investigate the full text corpus with text mining, which is an essential tool of Educational Data Mining.General scientometric indicators and tendencies such as the amount and length of the articles, the most significant authors’ impacts and backgrounds and the number of citations by authors have also been discussed. Moreover, we have analysed the ratio of male and female authors; nationality and the institutional background of certain researchers in a full range of metadata analysis.Recent studies has verified that scientific cooperation is growing world-wide; therefore, the first research question focuses on this matter, revealing the co-authorship network of the journal. The hubs of this graph are the most central persons in the collaborative authorship in the field of Hungarian educational research as regards to the analysed journal.Finally, after creating a co-authorship graph, an enormous citation graph has also been created in order to reveal the scientific network within the field of educational research in Hungary based on the analysed journal. Using this graph, a multi-criteria citation analyses has been conducted which could indicate additional relevant results.

Author Information
Gyula Nagy, University of Szeged, Hungary

Paper Information
Conference: ECE2018
Stream: Educational Research, Development & Publishing

This paper is part of the ECE2018 Conference Proceedings (View)
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Posted by James Alexander Gordon