Eğitimde Makine Öğrenmesi: Bir Bilim Haritalama Çalışması

Year 2024, Volume: 11 Issue: 1, 10 - 25, 31.01.2024

Vahid Sinap

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

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

References

• Alpaydin, E. (2020). Introduction to machine learning. MIT Press.
• Anozie, N., & Junker, B. W. (2006). Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system. Educational data mining: papers from the AAAI workshop içinde. AAAI Press.
• Börner, K. (2010). Atlas of Science: Visualizing What We Know. MIT Press.
• Becker, L. A. (1987). A framework for intelligent instructional systems: an artificial intelligence machine learning approach. PLET: Programmed Learning & Educational Technology, 24(2), 128-136.
• Blease, C., Kharko, A., Annoni, M., Gaab, J., & Locher, C. (2021). Machine learning in clinical psychology and psychotherapy education: a mixed methods pilot survey of postgraduate students at a Swiss university. Frontiers in Public Health, 273.
• Cardona, T., Cudney, E. A., Hoerl, R., & Snyder, J. (2020). Data mining and machine learning retention models in higher education. Journal of College Student Retention: Research, Theory & Practice, 0(0).
• Chen, C. (2006). Citespace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
• Dambic, G., Krajcar, M., & Bele, D. (2016). Machine learning model for early detection of higher education students that need additional attention in introductory programming courses. International Journal of Digital Technology & Economy, 1(1), 1-11.
• Dhal, P., & Azad, C. (2021). A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence, 52, 4543–4581.
• Eggert, K. (2021, November). How artificial intelligence will shape universities of tomorrow. In 2021 International Conference (p. 50).
• El-Alfy, E. S. M., & Abdel-Aal, R. E. (2008). Construction and analysis of educational tests using abductive machine learning. Computers & Education, 51(1), 1-16.
• Er, E. (2012). Identifying at-risk students using machine learning techniques: A case study with IS 100. International Journal of Machine Learning and Computing, 2(4), 476.
• Ferster, B. (2014). Teaching machines: learning from the intersection of education and technology. JHU Press.
• Gruijters, R. J., Chan, T. W., & Ermisch, J. (2019). Trends in educational mobility: How does China compare to Europe and the United States?. Chinese Journal of Sociology, 5(2), 214-240.
• Harvard Medical School (2022). Education & admissions. https://hms.harvard.edu/education
• Hazelkorn, E., Locke, W., Coates, H., & de Wit, H. (2022). Unprecedented challenges to higher education systems and academic collaboration. Policy Reviews in Higher Education, 6(2), 125-127.
• Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Grantee Submission, 6(2), 27-52.
• IMF. (2022). Advanced economies. https://www.imf.org/en/Publications/WEO/weo-database/2022/October/select-country-group
• Kaddoura, S., Popescu, D. E., & Hemanth, J. D. (2022). A systematic review on machine learning models for online learning and examination systems. PeerJ Computer Science, 8(Ml), e986. https://doi.org/10.7717/peerj-cs.986
• Kasemodel, M.G.C., Makishi, F., Souza, R.C., & Silva, V.L. (2016). Following the trail of crumbs: a bibliometric study on consumer behavior in the Food Science and Technology field. International Journal of Food Studies, 5(1), 73–83.
• Kolachalama, V. B., & Garg, P. S. (2018). Machine learning and medical education. NPJ digital medicine, 1(1), 54.
• Korkmaz, C., & Correia, A. P. (2019). A review of research on machine learning in educational technology. Educational Media International, 56(3), 250-267.
• Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
• Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behaviour & Information Technology, 38(4), 410-421.
• Lee, J. J., & Haupt, J. P. (2020). Winners and losers in US-China scientific research collaborations. Higher Education, 80, 57-74.
• Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348-362.
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. http://discovery.ucl.ac.uk/1475756/
• Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.
• Martí-Parreño, J., Méndez-Ibáñez, E., & Alonso-Arroyo, A. (2016). The use of gamification in education: a bibliometric and text mining analysis. Journal of Computer Assisted Learning, 32(6), 663–676.
• Nafea, I. T. (2018). Machine learning in educational technology. Machine Learning-Advanced Techniques and Emerging Applications, 175-183.
• Noyons, E. C. M., Moed, H. F., & Luwel, M. (1999). Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study. Journal of the American Society for Information Science, 50(2), 115-131.
• Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233-241.
• Peters, H.P.F. & Van Raan, A.F.J. (1991). Structuring scientific activities by co-author analysis an exercise on a university faculty level. Scientometrics, 20(1), 235–255.
• Ross, L. F., Loup, A., Nelson, R. M., Botkin, J. R., Kost, R., Smith Jr, G. R., & Gehlert, S. (2010). The challenges of collaboration for academic and community partners in a research partnership: Points to consider. Journal of Empirical Research on Human Research Ethics, 5(1), 19-31.
• Samuel, A.L. (1959) Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210-229.
• Seonghee, K., & Boryung, J. (2008). An analysis of faculty perceptions: Attitudes toward knowledge sharing and collaboration in an academic institution. Library & Information Science Research, 30(4), 282-290.
• Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(3), 265-269.
• Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813.
• Tajmir, S. H., & Alkasab, T. K. (2018). Toward augmented radiologists: changes in radiology education in the era of machine learning and artificial intelligence. Academic radiology, 25(6), 747-750.
• Tolsgaard, M. G., Boscardin, C. K., Park, Y. S., Cuddy, M. M., & Sebok-Syer, S. S. (2020). The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. Advances in Health Sciences Education, 25(5), 1057-1086.
• Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. Ding Y., Rousseau R., Wolfram D. (Ed.) içinde Measuring Scholarly Impact (ss. 285-320). Springer, Cham.
• Wekerle, C., Daumiller, M., & Kollar, I. (2022). Using digital technology to promote higher education learning: The importance of different learning activities and their relations to learning outcomes. Journal of Research on Technology in Education, 54(1), 1-17.
• Zhai, X., C Haudek, K., Shi, L., H Nehm, R., & Urban‐Lurain, M. (2020). From substitution to redefinition: A framework of machine learning‐based science assessment. Journal of Research in Science Teaching, 57(9), 1430-1459.