Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

• Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
• Alpaydin, E. (2020). Introduction to machine learning. MIT Press.
• Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 291–300. https://doi.org/10.1109/ICSE-SEIP.2019.00042
• Baraibar-Diez, E., Luna, M., Odriozola, M. D., & Llorente, I. (2020). Mapping social impact: A bibliometric analysis. In Sustainability (Switzerland) (Vol. 12, Issue 22, pp. 1–21). MDPI. https://doi.org/10.3390/su12229389
• Bartram, S. M., Branke, J., Rossi, G. De, & Motahari, M. (2021). Machine learning for active portfolio management. The Journal of Financial Data Science, 3(3), 9–30. https://doi.org/10.3905/jfds.2021.1.071
• Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11
• Caputo, A., & Kargina, M. (2022). A user-friendly method to merge Scopus and Web of Science data during bibliometric analysis. Journal of Marketing Analytics, 10(1), 82–88. https://doi.org/10.1057/s41270-021-00142-7
• Chaipidech, P., Srisawasdi, N., Kajornmanee, T., & Chaipah, K. (2022). A personalized learning system-supported professional training model for teachers’ TPACK development. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100064
• Darling-Hammond, L. (2017). Teacher education around the world: What can we learn from international practice? European Journal of Teacher Education, 40(3), 291–309. https://doi.org/10.1080/02619768.2017.1315399
• Díaz Redondo, R. P., Caeiro Rodríguez, M., López Escobar, J. J., & Fernández Vilas, A. (2021). Integrating micro-learning content in traditional e-learning platforms. Multimedia Tools and Applications, 80(2), 3121–3151. https://doi.org/10.1007/s11042-020-09523-z
• Dong, F., & Zhang, Y. (2016). Automatic features for essay scoring-an empirical study. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1072–1077. https://www.kaggle.com/c/asap-aes/data
• 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
• Ellegaard, O., & Wallin, J. A. (2015a). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
• Ellegaard, O., & Wallin, J. A. (2015b). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
• Fidan, M. (2023). The effects of microlearning-supported flipped classroom on pre-service teachers’ learning performance, motivation and engagement. Education and Information Technologies, 28(10), 12687–12714. https://doi.org/10.1007/s10639-023-11639-2
• Fu, S. (2022). A reinforcement learning-based smart educational environment for higher education. International Journal of E-Collaboration, 19(6), 1–17. https://doi.org/10.4018/IJeC.315019
• Garcia-Garcia, J. M., Penichet, V. M. R., Lozano, M. D., Garrido, J. E., & Law, E. L. C. (2018). Multimodal affective computing to enhance the user experience of educational software applications. Mobile Information Systems, 2018. https://doi.org/10.1155/2018/8751426
• Gardner, J., O’Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’ In Journal of Computer Assisted Learning (Vol. 37, Issue 5, pp. 1207–1216). John Wiley and Sons Inc. https://doi.org/10.1111/jcal.12577
• Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Whye Teh, Y., Rezende, D. J., & Eslami, S. M. A. (2018). Conditional neural processes. International Conference on Machine Learning, 1704–1713.
• He, Z., Xia, W., Dong, K., Guo, H., Tang, R., Xia, D., & Zhang, R. (2022). Unsupervised learning style classification for learning path generation in online education platforms. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2997–3006. https://doi.org/10.1145/3534678.3539107
• Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018). Predicting academic performance: A systematic literature review. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 175–199. https://doi.org/10.1145/3293881.3295783
• Hilbert, S., Coors, S., Kraus, E., Bischl, B., Lindl, A., Frei, M., Wild, J., Krauss, S., Goretzko, D., & Stachl, C. (2021). Machine learning for the educational sciences. In Review of Education (Vol. 9, Issue 3). John Wiley and Sons Inc. https://doi.org/10.1002/rev3.3310
• Inyega, H. N., & Inyega, J. O. (2020). Machine learning: the future of sustainable teacher education is here. Journal of Pedagogy, Andragogy and Heutagogy in Academic Practice, 1(2).
• Jing, Y., Wang, C., Chen, Y., Wang, H., Yu, T., & Shadiev, R. (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
• Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159–190. https://doi.org/10.1007/s10462-007-9052-3
• Mousavi, S. S., Schukat, M., & Howley, E. (2018, June 22). Deep reinforcement learning: An overview. Proceedings of SAI Intelligent Systems Conference. https://doi.org/10.1007/978-3-319-56991-8_32
• Murphy, R. F. (2019). Artificial intelligence applications to support k-12 teachers and teaching: a review of promising applications, opportunities, and challenges. https://www.jstor.org/stable/pdf/resrep19907.pdf
• Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. In Applied Sciences (Switzerland) (Vol. 11, Issue 1, pp. 1–28). MDPI AG. https://doi.org/10.3390/app11010237
• Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N. R., Visvizi, A., & Hassan, S. U. (2022). Leveraging ai and machine learning for national student survey: actionable insights from textual feedback to enhance quality of teaching and learning in UK’s higher education. Applied Sciences (Switzerland), 12(1). https://doi.org/10.3390/app12010514
• Nye, B. D. (2015). Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education, 25(2), 177–203. https://doi.org/10.1007/s40593-014-0028-6
• Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48. https://doi.org/10.14445/22312803/IJCTT-V48P126
• Purnama Sari, I., & Hanif Batubara, I. (2021). Cluster analysis using k-means algorithm and fuzzy C-means clustering for grouping students’ abilities in online learning process. Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE), 2(1), 139–144. https://doi.org/10.30596/jcositte.v2i1.6504
• Sajjadi, M., Javanmardi, M., & Tasdizen, T. (2016). Regularization with stochastic transformations and perturbations for deep semi-supervised learning. 30th Conference on Neural Information Processing Systems, 1–9.
• Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00592-x
• Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised Classification Algorithms in Machine Learning: A Survey and Review (pp. 99–111). https://doi.org/10.1007/978-981-13-7403-6_11
• Shetty, S. H., Shetty, S., Singh, C., & Rao, A. (2022). Supervised machine learning: Algorithms and applications. In fundamentals and methods of machine and deep learning (pp. 1–16). Wiley. https://doi.org/10.1002/9781119821908.ch1
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
• Tammets, K., & Ley, T. (2023). Integrating AI tools in teacher professional learning: a conceptual model and illustrative case. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1255089
• Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021). Personalized and adaptive learning. J. Ryoo, K. Winkelmann (eds.), Innovative Learning Environments in STEM Higher Education (pp. 17–34)., SpringerBriefs Statistics, https://doi.org/10.1007/978-3-030-58948-6_2
• Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning ai for k-12: what should every child know about ai? Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795
• Waheed, H., Hassan, S. U., Aljohani, N. R., & Wasif, M. (2018). A bibliometric perspective of learning analytics research landscape. Behaviour and Information Technology, 37(10–11), 941–957. https://doi.org/10.1080/0144929X.2018.1467967
• Yakubu, M. N., & Abubakar, A. M. (2022). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, 51(2), 916–934. https://doi.org/10.1108/K-12-2020-0865

APA

Demir, S. (2023). Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches. Journal of Research in Didactical Sciences, 2(1), 14202. https://doi.org/10.51853/jorids/14202

Vancouver

Demir S. Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches. Journal of Research in Didactical Sciences. 2023;2(1):14202. https://doi.org/10.51853/jorids/14202

AMA

Demir S. Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches. Journal of Research in Didactical Sciences. 2023;2(1), 14202. https://doi.org/10.51853/jorids/14202

Chicago

Demir, Servet. "Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches". Journal of Research in Didactical Sciences 2023 2 no. 1 (2023): 14202. https://doi.org/10.51853/jorids/14202

Harvard

Demir, S. (2023). Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches. Journal of Research in Didactical Sciences, 2(1), 14202. https://doi.org/10.51853/jorids/14202

MLA

Demir, Servet "Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches". Journal of Research in Didactical Sciences, vol. 2, no. 1, 2023, 14202. https://doi.org/10.51853/jorids/14202