Machine Learning in Education: A Science Mapping Study

Abstract

The rapidly developing field of machine learning has piqued the interest of many academic disciplines in recent years. Research on machine learning, especially in the realm of education, is experiencing rapid growth. Determining the current state and development of machine learning in the field of education will provide a comprehensive roadmap for researchers in this domain. The aim of this research is to analyze publications on machine learning in education in terms of their main topics and international collaboration processes, as well as to identify trends in the field. In the Web of Science database between 2002 and 2022, scientific documents containing keywords such as "machine learning" and "education," or related terms like "educational" or "instructional" in their bibliographic tags, constitute the dataset for this research. The bibliographic information of 2,851 studies obtained after conducting the queries and necessary processing forms the research dataset. The research utilizes bibliometric analysis methods, including co-word analysis, co-authorship analysis, citation analysis, and co-citation analysis. According to the results, the most frequently studied scientific concepts related to machine learning are "machine learning" and "artificial intelligence." On the educational side, concepts such as "educational data mining" and "learning analytics" are commonly used. Furthermore, the most productive and highly cited research originates from the USA and China. The number of studies conducted has gained momentum in the last five years. These studies span various academic fields, including educational technology, computer science, informatics, natural sciences, mathematics, engineering, and health.

Keywords

• Education
• machine learning
• artificial intelligence
• bibliometrics
• science mapping

Eğitimde Makine Öğrenmesi: Bir Bilim Haritalama Çalışması

Abstract

Hızla gelişmekte olan makine öğrenmesi, son yıllarda birçok akademik çalışma alanının ilgisi haline gelmiştir. Özellikle eğitim alanında makine öğrenmesi üzerine gerçekleştirilen araştırmalar süratle artmaktadır. Makine öğrenmesinin eğitim alanındaki gelişiminin ve mevcut durumunun belirlenmesi alandaki araştırmacılara kapsamlı bir yol haritası sunacaktır. Bu kapsamda bu araştırmanın amacı, eğitimde makine öğrenmesi konulu yayınları başlıca çalışılan bilimsel olgu ve kavramlar ile uluslararası iş birliği süreçleri bakımından incelemek, alandaki eğilimleri tespit etmektir. Araştırmanın verilerini; Web of Science veri tabanında dizinlenmiş, 2002-2022 yılları arasında yayımlanan, bibliyografik künye bilgilerinde “makine öğrenmesi” ile “eğitim”, “eğitsel” veya “öğretim” anahtar kelimeleri geçen ve araştırma kriterlerini sağlayan 2851 bilimsel belgenin bibliyografik verileri oluşturmaktadır. Araştırmada bibliyometrik analiz yöntemlerinden ortak kelime, ortak yazarlık, atıf ve ortak atıf analizleri kullanılmıştır. Elde edilen sonuçlara göre, makine öğrenmesi tarafında en çok çalışılan bilimsel olgu ve kavramlar “makine öğrenmesi” ve “yapay zekâ” olmuştur. Eğitim tarafında ise “eğitsel veri madenciliği” ve “öğrenme analitiği” kavramları sıklıkla kullanılmıştır. Ayrıca, en üretken ve araştırmaları en çok atıf alan ülkeler ABD ile Çin’dir. Yapılan araştırmaların sayısı son beş yıl içerisinde ciddi bir ivme kazanmıştır. Yapılan çalışmalar eğitim teknolojisi, bilgisayar bilimi, bilişim, fen bilimleri, matematik, mühendislik ve sağlık gibi birçok çeşitli akademik alanla ilişkili haldedir.

Keywords

• eğitim
• makine öğrenmesi
• yapay zekâ
• bibliyometri
• bilim haritalama

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