Abstract
This study leverages bibliometric analysis through the bibliometrix R-package to dissect the expansive influence of machine learning on education, a field where machine learning’s adaptability and data-processing capabilities promise to revolutionize teaching and learning methods. Despite its potential, the integration of machine learning in education requires a nuanced understanding to navigate the associated challenges and ethical considerations effectively. Our investigation spans articles from 2000 to 2023, focusing on identifying growth patterns, key contributors, and emerging trends within this interdisciplinary domain. By analyzing 970 selected articles, this study uncovers the developmental trajectory of machine learning in education, revealing significant insights into publication trends, prolific authors, influential institutions, and the geographical distribution of research. Furthermore, it highlights the journals pivotal in disseminating machine learning education research, the most cited works that shape the field, and the dynamic evolution of research themes. This bibliometric exploration not only charts the current landscape but also anticipates future directions, suggesting areas for further inquiry and potential breakthroughs. Through a detailed examination of empirical evidence and a critical analysis of machine learning applications in educational settings, this study aims to provide a foundational understanding of the field’s complexities and potentials. The anticipated outcome is a comprehensive roadmap that guides researchers, educators, and policymakers towards a thoughtful integration of machine learning in education, balancing innovation with ethical stewardship.











Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
We make sure that all data and materials support our published claims and comply with feld standards.
References
Alshamaila, Y., Alsawalqah, H., Aljarah, I., Habib, M., Faris, H., Alshraideh, M., & Salih, B. A. (2024). An automatic prediction of students’ performance to support the university education system: A deep learning approach. Multimedia Tools and Applications. Advance online publication. https://doi.org/10.1007/s11042-024-18262-4.
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007.
Basnet, R. B., Johnson, C., & Doleck, T. (2022). Dropout prediction in Moocs using deep learning and machine learning. Education and Information Technologies, 27, 11499–11513. https://doi.org/10.1007/s10639-022-11068-7.
Botvin, M., Hershkovitz, A., & Forkosh-Baruch, A. (2023). Data-driven decision-making in emergency remote teaching. Education and Information Technologies, 28, 489–506. https://doi.org/10.1007/s10639-022-11176-4.
Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., & Edwards, T. C. (2015). Machine learning for predicting soil classes in three semi-arid landscapes (Vol. 239–240, pp. 68–83). Geoderma. https://doi.org/10.1016/j.geoderma.2014.09.019.
Carcillo, F., Borgne, Y., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317–331. https://doi.org/10.1016/j.ins.2019.05.042.
Chen, Z., Zhao, P., Li, F., Marquez-Lago, T. T., Leier, A., Revote, J., et al. (2020). iLearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in Bioinformatics, 21(3), 1047–1057. https://doi.org/10.1093/bib/bbz041.
Choi, H., Lee, H., & Lee, M. (2023). Optimal knowledge component extracting Model for Knowledge-Concept Graph Completion in Education. Ieee Access: Practical Innovations, Open Solutions, 11, 15002–15013. https://doi.org/10.1109/ACCESS.2023.3244614.
Cui, X., & Chen, M. (2024). A novel learning framework for vocal music education: An exploration of convolutional neural networks and pluralistic learning approaches. Soft Computing. Advance online publication. https://doi.org/10.1007/s00500-023-09618-3.
Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506. https://doi.org/10.1016/j.dss.2010.06.003.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070.
Dworkin, J. D., Linn, K. A., Teich, E. G., Zurn, P., Shinohara, R. T., & Bassett, D. S. (2020). The extent and drivers of gender imbalance in neuroscience reference lists. Nature Neuroscience, 23(8), 918–926. https://doi.org/10.1038/s41593-020-0658-y.
Earhart, A., Risam, R., & Bruno, M. (2021). Citational politics: Quantifying the influence of gender on citation in Digital Scholarship in the humanities. Digital Scholarship in the Humanities, 36(3), 581–594. https://doi.org/10.1093/llc/fqaa011.
Evangelista, E. (2021). A hybrid machine learning Framework for Predicting Students’ performance in virtual learning environment. International Journal of Emerging Technologies in Learning (iJET), 16(24), 255–272. https://doi.org/10.3991/ijet.v16i24.26151.
Fernández-Morante, C., Cebreiro-López, B., Rodríguez-Malmierca, M. J., & Casal-Otero, L. (2021). Adaptive learning supported by Learning Analytics for Student teachers’ personalized training during in-School practices. Sustainability, 14(1), 124. https://doi.org/10.3390/su14010124.
Gordon, C., & Debus, R. (2002). Developing deep learning approaches and personal teaching efficacy within a preservice teacher education context. British Journal of Educational Psychology, 72, 483–511. https://doi.org/10.1348/00070990260377488.
Greener, S. (2022). Evaluating literature with bibliometrics. Interactive Learning Environments, 30(5), 1168–1169. https://doi.org/10.1080/10494820.2022.2118463.
Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O., Rodrigo, M. T., Cukurova, M., Bittencourt, I., & Koedinger, K. (2021). Ethics of AI in education: Towards a community-wide Framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1.
Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review, 52(1), 381–407. https://doi.org/10.1007/s10462-018-9620-8.
Hussain, S., Gaftandzhieva, S., Maniruzzaman, M., et al. (2021). Regression analysis of student academic performance using deep learning. Education and Information Technologies, 26, 783–798. https://doi.org/10.1007/s10639-020-10241-0.
Jing, Y., Wang, C., Chen, Y. (2023). Bibliometric mapping techniques in educational technology research: A systematic literature review. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12178-6.
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017.
Kimmons, R., Rosenberg, J., & Allman, B. (2021). Trends in educational technology: What Facebook, Twitter, and Scopus can tell us about current research and practice. TechTrends, 65, 125–136. https://doi.org/10.1007/s11528-021-00589-6.
Kokol, P., Kokol, M., & Zagoranski, S. (2022). Machine learning on small size samples: A synthetic knowledge synthesis. Science Progress, 105(1), 00368504211029777. https://doi.org/10.1177/00368504211029777.
Korkmaz, C., & Correia, A. P. (2019). A review of research on machine learning in educational technology. Educational Media International, 56(3), 250–267. https://doi.org/10.1080/09523987.2019.1669875.
Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting Students’ performance in Distance Learning using machine learning techniques. Applied Artificial Intelligence, 18(5), 411–426. https://doi.org/10.1080/08839510490442058.
Liu, M., & Yu, D. (2023). Towards intelligent E-learning systems. Education and Information Technologies, 28, 7845–7876. https://doi.org/10.1007/s10639-022-11479-6.
Liu, S., Shi, Q., & Zhang, L. (2021). Few-shot hyperspectral image classification with unknown classes using Multitask Deep Learning. IEEE Transactions on Geoscience and Remote Sensing, 59(6), 5085–5102. https://doi.org/10.1109/TGRS.2020.3018879.
Mao, G., Hu, H., Liu, X., Crittenden, J., & Huang, N. (2021). A bibliometric analysis of industrial wastewater treatments from 1998 to 2019. Environmental Pollution, 275, 115785. https://doi.org/10.1016/j.envpol.2020.115785.
Masiero, S., & Aaltonen, A. (2021). Assessing Gender Bias in the Information Systems Field: An Analysis of the Impact on Citations. ArXiv. https://doi.org/10.48550/arXiv.2108.12255.
Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional De La Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03.
Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning approaches in digital education: A systematic revision. Information, 13(4), 203. https://doi.org/10.3390/info13040203.
Nagao, K. (2019). Artificial intelligence accelerates human learning: Discussion data analytics. Springer. https://doi.org/10.1007/978-981-13-6175-3.
Odden, T. O. B., Marin, A., & Caballero, M. D. (2020). Thematic analysis of 18 years of physics education research conference proceedings using natural language processing. Physical Review Physics Education Research, 16(1), 010142. https://doi.org/10.1103/PhysRevPhysEducRes.16.010142.
Oducado, R. M. F., Dequilla, M. A. C. V., & Villaruz, J. F. (2022). Factors predicting videoconferencing fatigue among higher education faculty. Education and Information Technologies, 27, 9713–9724. https://doi.org/10.1007/s10639-022-11017-4.
Oliveira, O., Silva, F. F., Juliani, F., Barbosa, L. C., & Nunhes, T. (2019). Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects. In S. Kunosic, & E. Zerem (Eds.), Scientometrics Recent Advances. IntechOpen. https://doi.org/10.5772/intechopen.85856.
Qian, K., & Zhong, Z. (2023). Research frontiers of electroporation-based applications in cancer treatment: A bibliometric analysis. Biomedical Engineering/Biomedizinische Technik, 68(5), 445–456. https://doi.org/10.1515/bmt-2023-0113.
Raihan, M. Z., & Azad, M. A. (2021). A bibliometric review on outcome-based learning for graduate employability: Mapping the research front. Journal of Education, 203(1), 73–91. https://doi.org/10.1177/00220574211016444.
Ranjeeth, S., Latchoumi, T. P., & Paul, P. V. (2021). Optimal stochastic gradient descent with multilayer perceptron based student’s academic performance prediction model. Recent Advances in Computer Science and Communications, 14(6), 1728–1741. https://doi.org/10.2174/2666255813666191116150319.
Riemann, S., Roheger, M., Kohlschmidt, J., Kirschke, J., Lillo, M., Flöel, A., & Meinzer, M. (2022). Gender imbalances in citation rates are mediated by field specific author gender distributions. bioRxiv. https://doi.org/10.1101/2022.07.28.501862.
Sabharwal, R., & Miah, S. J. (2024). Evaluating teachers’ effectiveness in classrooms: An ML-based assessment portfolio. Social Network Analysis and Mining, 14(1), 28. https://doi.org/10.1007/s13278-023-01195-5.
Saltz, J., Skirpan, M., Fiesler, C., Gorelick, M., Yeh, T., Heckman, R., Dewar, N. I., & Beard, N. (2019). Integrating Ethics within Machine Learning courses. ACM Transactions on Computing Education, 19(4), 32. https://doi.org/10.1145/3341164.
Sommer, C., & Gerlich, D. W. (2013). Machine learning in cell biology – teaching computers to recognize phenotypes. Journal of Cell Science, 2013, 126(24), 5529–5539. https://doi.org/10.1242/jcs.123604.
Su, M., Peng, H., & Li, S. (2021). A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE). Expert Systems with Applications, 186, 115728. https://doi.org/10.1016/j.eswa.2021.115728.
Su, Y. S., Lin, Y. D., & Liu, T. Q. (2022). Applying machine learning technologies to explore students’ learning features and performance prediction. Frontiers in Neuroscience, 16, 1018005. https://doi.org/10.3389/fnins.2022.1018005.
Tekles, A., Auspurg, K., & Bornmann, L. (2022). Same-gender citations do not indicate a substantial gender homophily bias. PLOS ONE, 17(9), e0274810. https://doi.org/10.1371/journal.pone.0274810.
Tiwari, R. (2023). The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences. International Journal of Scientific Research in Engineering and Management, 7(2), 1. https://doi.org/10.55041/ijsrem17645.
Vos, N., van der Meijden, H., & Denessen, E. (2011). Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers & Education, 56(1), 127–137. https://doi.org/10.1016/j.compedu.2010.08.013.
Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189. https://doi.org/10.1016/j.chb.2019.106189.
Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2021). Machine learning for human learners: Opportunities, issues, tensions and threats. Educational Technology Research and Development, 69(4), 2109–2130. https://doi.org/10.1007/s11423-020-09858-2.
Xu, S., & Yin, X. (2022). Recommendation system for privacy-preserving education technologies. Computational Intelligence and Neuroscience, 2022, 3502992. https://doi.org/10.1155/2022/3502992.
Yang, J., DeVore, S., Hewagallage, D., Miller, P., Ryan, Q. X., & Stewart, J. (2020). Using machine learning to identify the most at-risk students in physics classes. Physical Review Physics Education Research, 16(2), 020130. https://doi.org/10.1103/PhysRevPhysEducRes.16.020130.
Yang, C., Chiang, F. K., Cheng, Q., & Ji, J. (2021). Machine learning-based student modeling methodology for intelligent tutoring systems. Journal of Educational Computing Research, 59(6), 1015–1035. https://doi.org/10.1177/0735633120986256.
Zeng, K., Zhang, Q., Chen, B., Liang, B., & Yang, J. (2022). APD: Learning diverse behaviors for reinforcement learning through unsupervised active pre-training. IEEE Robotics and Automation Letters, 7(4), 12251–12258. https://doi.org/10.1109/LRA.2022.3214057.
Zhong, Z., & Fan, L. (2023). Worldwide Trend Analysis of Psycholinguistic Research on Code switching using bibliometrix R-tool. SAGE Open, 13(4). https://doi.org/10.1177/21582440231211657.
Acknowledgements
We thank editors and reviewers for their valuable feedback, which has greatly improved this manuscript. Their insights and suggestions have been instrumental in refining our work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
We have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhong, Z., Guo, H. & Qian, K. Deciphering the impact of machine learning on education: Insights from a bibliometric analysis using bibliometrix R-package. Educ Inf Technol 29, 21995–22022 (2024). https://doi.org/10.1007/s10639-024-12734-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-024-12734-8