The Future of Distance Education: Exploring the Potential of Adaptive Learning Systems

Öz

Adaptive learning technologies are used in many areas of education, including online distance learning. This study investigates the applications of Adaptive Learning technology in distance education environments. Following the steps of systematic literature review and using bibliometric analysis, the study examines a total of 1071 publications. Accordingly, time trend analysis has increased steadily within recent years, and Educational Research, Computer Science, and Engineering are the leading subject areas in research on Adaptive Learning in distance education. China and USA are the countries that make the most of the contribution, followed by Taiwan, Spain, and India. Kinshuk is the author who contributed the most, followed by Caballé and Santi. The most collaborative authors are Capuano and Nicola, Ritrovato and Pierluigi, Cabelle and Santi, and Gaeta and Matteo. Pierri and Anna hold central positions in the co-authoring network. The top three journals with the most publications are Computers & Education, International Journal of Distance Education, and Academic Medicine, all with significant citation counts. The most frequently used keywords are “e-learning”, followed by “adaptive learning”, and “online learning”. Integrating artificial intelligence and machine learning techniques presents significant potential for enhancing adaptive learning technologies. Utilizing frameworks such as learning analytics and the Technology Acceptance Model can help identify effective strategies to increase system acceptability.

Anahtar Kelimeler

• adaptive learning
• distance education
• systematic literature review
• bibliometric analysis

Uzaktan Eğitimin Geleceği: Uyarlanabilir Öğrenme Sistemlerinin Potansiyelini Keşfetmek

Öz

Uyarlanabilir öğrenme teknolojileri, çevrim içi uzaktan eğitim de dahil olmak üzere birçok eğitim alanında kullanılmaktadır. Bu çalışma, uyarlanabilir öğrenme teknolojisinin uzaktan eğitim ortamlarındaki uygulamalarını araştırmaktadır. Sistematik literatür taraması adımlarını izleyerek ve bibliyometrik analiz kullanarak, çalışma toplamda 1071 yayını incelemektedir. Buna göre, son yıllarda zaman eğilimi analizi istikrarlı bir artış göstermiş olup, uzaktan eğitimde uyarlanabilir öğrenme üzerine yapılan araştırmalarda Eğitim Araştırmaları, Bilgisayar Bilimleri ve Mühendislik önde gelen konu alanlarıdır. Çin ve ABD en fazla katkıda bulunan ülkeler olup, onları Tayvan, İspanya ve Hindistan takip etmektedir. En çok katkıda bulunan yazar Kinshuk iken, onu Caballé ve Santi izlemektedir. En çok iş birliği yapan yazarlar Capuano ve Nicola, Ritrovato ve Pierluigi, Caballé ve Santi ile Gaeta ve Matteo'dur. Pierri ve Anna, ortak yazar ağında merkezi konumlara sahiptir. En fazla yayına sahip ilk üç dergi, önemli atıf sayılarıyla Computers & Education, International Journal of Distance Education ve Academic Medicine'dir. En sık kullanılan anahtar kelimeler “e-öğrenme”, ardından “uyarlanabilir öğrenme” ve “çevrimiçi öğrenme”dir. Yapay zekâ ve makine öğrenimi tekniklerinin entegrasyonu, uyarlanabilir öğrenme teknolojilerini geliştirme potansiyelini önemli ölçüde artırmaktadır. Öğrenme analitikleri ve Teknoloji Kabul Modeli gibi çerçevelerin kullanılması, sistemin kabul edilebilirliğini artıracak etkili stratejilerin belirlenmesine yardımcı olabilir.

Anahtar Kelimeler

• uyarlanabilir öğrenme
• uzaktan eğitim
• sistematik literatür incelemesi
• bibliyometrik analiz

Kaynakça

1. Adnan, M., Alsaeed, D. H., Al-Baity, H. H., & Rehman, A. (2021). Leveraging the power of deep learning technique for creating an intelligent, context-aware, and adaptive M-Learning model. Complexity, 2021. https://doi.org/10.1155/2021/5519769
2. Bordignon, V., Matta, V., & Sayed, A. H. (2021). Adaptive social learning. IEEE Transactions on Information Theory, 67(9), 6053–6081. https://doi.org/10.1109/TIT.2021.3094633
3. Brusilovsky, P. (1998). Methods and techniques of adaptive hypermedia. In P. Brusilovsky, A. Kobsa, & J. Vassileva (Eds.), Adaptive Hypertext and Hypermedia (pp. 1–43). Springer Netherlands. https://doi.org/10.1007/978-94-017-0617-9_1
4. Brusilovsky, P., & Pesin, L. (1998). Adaptive navigation support in educational hypermedia: An evaluation of the ISIS-tutor. Journal of Computing and Infrmation Technology, 1, 27–38. https://hrcak.srce.hr/file/221190 Capuano, N., & Caballé, S. (2020). Adaptive learning technologies. AI Magazine, 41(2), 96–98. https://doi.org/10.1609/aimag.v41i2.5317
5. Da Silva, L. M., Dias, L. P. S., Rigo, S., Barbosa, J. L. V., Leithardt, D. R. F., & Leithardt, V. R. Q. (2021). A literature review on intelligent services applied to distance learning. In Education Sciences (Vol. 11, Issue 11). MDPI. https://doi.org/10.3390/educsci11110666
6. Demartini, C. G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial intelligence bringing improvements to adaptive learning in education: A case study. Sustainability, 16(3). https://doi.org/10.3390/su16031347
7. Doğan, M. E., Görü Doğan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5). https://doi.org/10.3390/app13053056
8. Dziuban, C., Moskal, P., Parker, L., Campbell, M., Howlin, C., & Johnson, C. (2018). Adaptive learning: A stabilizing influence across disciplines and universities. Online Learning Journal, 22(3), 7–39. https://doi.org/10.24059/olj.v22i3.1465

9. Essa, S. G., Celik, T., & Human-Hendricks, N. E. (2023). Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: A systematic literature review. IEEE Access, 11, 48392–48409. https://doi.org/10.1109/ACCESS.2023.3276439
10. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317. https://doi.org/10.1504/IJTEL.2012.051816
11. García-Tudela, P. A., Prendes-Espinosa, P., & Solano-Fernández, I. M. (2023). The Spanish experience of future classrooms as a possibility of smart learning environments. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18577
12. Garrido, A., & Onaindia, E. (2013). Assembling learning objects for personalized learning: An ai planning perspective. IEEE Intelligent Systems, 28(2). https://doi.org/10.1109/MIS.2011.36
13. Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-Learning: A literature review. In Education Sciences (Vol. 13, Issue 12). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/educsci13121216
14. Global Education Report Team. (2024). Global education monitoring report, 2024/5, Leadership in education: Lead for learning. Unesco. https://doi.org/10.54676/EFLH5184
15. Hakimi, M., Katebzadah, S., & Fazil, A. W. (2024). Comprehensive insights into E-learning in contemporary education: Analyzing trends, challenges, and best practices. Journal Of Education And Teaching Learning (JETL), 6(1), 86–105. https://doi.org/10.51178/jetl.v6i1.1720
16. Haug, J., Fischer, D., & Hagel, G. (2023). Development of a short form of the ındex of learning styles for the use in adaptive learning systems. Proceedings of the 5th European Conference on Software Engineering Education, 194–198.
17. Huang, F., Feng, X. Y., Zhou, S. Sen, Tang, L. H., & Xia, Z. G. (2022). Establishing and applying an adaptive strategy and approach to eliminating malaria: practice and lessons learnt from China from 2011 to 2020. Emerging Microbes and Infections, 11(1), 314–325. https://doi.org/10.1080/22221751.2022.2026740
18. Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022. Sustainability, 15(4). https://doi.org/10.3390/su15043115
19. Kandemir, B., & Kılıç Çakmak, E. (2024). Transactional distance’s influence on students’ social, cognitive, teaching presence, and academic achievement. American Journal of Distance Education, 1–24. https://doi.org/10.1080/08923647.2024.2393490
20. Kerimbayev, N., Umirzakova, Z., Shadiev, R., & Jotsov, V. (2023). A student-centered approach using modern technologies in distance learning: A systematic review of the literature. In Smart Learning Environments (Vol. 10, Issue 1). Springer. https://doi.org/10.1186/s40561-023-00280-8
21. Li, F., He, Y., & Xue, Q. (2021). International forum of educational technology & society progress, challenges and countermeasures of adaptive learning. Technology & Society, 24(3), 238–255. https://doi.org/10.2307/27032868
22. Li, Y., Jiang, A., Li, Q., & Zhu, C. (2022). The analysis of research hot spot and trend on artificial ıntelligence in education. International Journal of Learning and Teaching, 8(1) 49–52. https://doi.org/10.18178/ijlt.8.1.49-52
23. Lin, C. F., Yeh, Y. C., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers and Education, 68, 199–210. https://doi.org/10.1016/j.compedu.2013.05.009
24. Liu, S., Zhang, X., Chen, W., & Zhang, W. (2021). Construction of intelligent adaptive learning platform in ubiquitous environment. 2021 10th International Conference on Educational and Information Technology, ICEIT 2021, 56–60. https://doi.org/10.1109/ICEIT51700.2021.9375613
25. Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903–1929. https://doi.org/10.1007/s11423-020-09793-2
26. Mezin, H., Kharrou, S. Y., & Lahcen, A. A. (2022). Adaptive learning algorithms and platforms: A concise overview. In Y. Maleh, M. Alazab, N. Gherabi, L. Tawalbeh, & A. A. Abd El-Latif (Eds.), Advances in Information, Communication and Cybersecurity (pp. 3–12). Springer International Publishing.
27. Miralrio, A., Muñoz-Villota, J., & Camacho-Zuñiga, C. (2024). From flexibility to adaptive learning: a pre-COVID-19 perspective on distance education in Latin America. In Frontiers in Computer Science (Vol. 6). Frontiers Media SA. https://doi.org/10.3389/fcomp.2024.1250992
28. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/j.ijsu.2010.02.007
29. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
30. Morze, N., Varchenko-Trotsenko, L., Terletska, T., & Smyrnova-Trybulska, E. (2021). Implementation of adaptive learning at higher education institutions by means of Moodle LMS. Journal of Physics: Conference Series, 1840(1). https://doi.org/10.1088/1742-6596/1840/1/012062
31. Muñoz, J. L. R., Ojeda, F. M., Jurado, D. L. A., Peña, P. F. P., Carranza, C. P. M., Berríos, H. Q., Molina, S. U., Farfan, A. R. M., Arias-Gonzáles, J. L., & Vasquez-Pauca, M. J. (2022). Systematic review of adaptive learning technology for learning in higher education. Eurasian Journal of Educational Research, 2022(98), 221–233. https://doi.org/10.14689/ejer.2022.98.014
32. Park, E., Ifenthaler, D., & Clariana, R. B. (2023). Adaptive or adapted to: Sequence and reflexive thematic analysis to understand learners’ self-regulated learning in an adaptive learning analytics dashboard. British Journal of Educational Technology, 54(1), 98–125. https://doi.org/10.1111/bjet.13287
33. Peng, Y., Yang, Z., Hou, J. L., Xu, J. R., Liu, S. T., & Ming, F. C. (2010). Complex adaptive organization change: an empirical study on Chinese telecom enterprise. Advanced Materials Research, 108, 1458–1464. https://doi.org/10.4028/www.scientific.net/amr.108-111.1458
34. Pressman, R. S. (2010). Software engineering: A practitioner’s approach (Seventh). McGraw-Hill. Randi, J. (2022). Adaptive teaching. In Adaptive Teaching. Routledge. https://doi.org/10.4324/9781138609877-ree125-1
35. Srisa-An, C., & Yongsiriwit, K. (2019). Applying machine learning and AI on self automated personalized online learning. Frontiers in Artificial Intelligence and Applications, 320. https://doi.org/10.3233/FAIA190174
36. van Alten, D. C. D., Phielix, C., Janssen, J., & Kester, L. (2020). Self-regulated learning support in flipped learning videos enhances learning outcomes. Computers and Education, 158. https://doi.org/10.1016/j.compedu.2020.104000
37. Wang, S. (2009). Adapting by learning: The evolution of China’s rural health care financing. Modern China, 35(4), 370–404. https://doi.org/10.1177/0097700409335381
38. Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
39. Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140. https://doi.org/10.1016/j.compedu.2019.103599
40. Yuan, H., Jiang, J., & Chen, D. (2023). Hot spot and development trend of adaptive learning in China based on citespace software. Frontiers in Educational Research, 6(8). https://doi.org/10.25236/FER.2023.060805