Betimleyici ve Metin Madenciliği Yöntemleri Kullanılarak Covid19 Konulu Eğitim Dergisi Yayınlarında Araştırma Eğilimleri Analizi: Ön Analiz

Year 2021, Issue: 29, 432 - 437, 01.12.2021

Cansu Çiğdem Ekin , Mustafa Çakıcı Egemen Şener Sıla Türker Sinem Altanlar

https://doi.org/10.31590/ejosat.1036109

Cited By: 1

Abstract

Çalışma, eğitim alanındaki dergilerde yer alan Covid19 ile ilgili yapılan çalışmaların profilini ortaya çıkarmayı amaçlamıştır. Bu amaçla, Ocak 2020 ile Mayıs 2021 tarihleri arasında SCOPUS veri tabanı tarafından indekslenen 3039 dergi makalesini analiz etmek için olasılıksal konu modelleme ve betimsel analiz ile birlikte kullanılmıştır. Betimsel analiz kapsamında en çok atıf alan dergiler, en çok yayın yapan dergiler ve en çok yayın yapan ülkeler analiz edilmiştir. Olasılıksal konu modelleme aşamasınde ise; başlıklarında covid, corona, pandemi gibi anahtar kelimeler içeren yayınlardaki çalışma konularını belirlemek için ilgili belgelerin özetlerine Latent Dirichlet Tahsisi (LDA) algoritması uygulanmaktadır. Metin madenciliği sonuçları, eğitim alanındaki dergilerde covid19 ile ilgili çalışmaların profilini haritalayan 10 ana konuyu ortaya koymuştur. Bu çalışmada ön analiz sonucları verilmiştir.

Keywords

COVID-19, Latent Dirichlet Algorithm, Online Eğitim, e-Learning, Digital Eğitim, Konu Modelleme

Research Trends Analysis in Educational Journal Publications on Covid19 Using Descriptive and Text Mining Methods :Preliminary Analysis

Abstract

The study aims to reveal the studies' profile on covid19 in journals in the field of education. For this purpose, probabilistic topic modeling technique and decriptive analysis has been used to together to analyze 3039 journal articles that are indexed by the SCOPUS database between January 2020 and May 2021. Within the scope of decriptive analysis, the most cited journals, the most publishing journals, and the most publishing countries were analyzed. In probabilistic topic modeling stage, Latent Dirichlet allocation (LDA) algorithm which is a text mining method was applied to the abstracts of those extracted documents to identify topics in publications containing keywords such as covid, corona, pandemic in their titles. The results of text mining revealed 10 major topics mapping the the studies' profile on covid19 in journals in the field of education. In this study, preliminary analysis results were given.

Keywords

COVID-19, Latent Dirichlet Algorithm, Online Education, e-Learning, Digital Education, Topic Modeling

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