Dynamic updating mechanism of teaching content driven by both artificial intelligence and big data within an efficient classroom

[1] Steenbeek, H., & van Geert, P. (2013). The emergence of learning-teaching trajectories in education: A complex dynamic systems approach. Nonlinear dynamics, psychology, and life sciences, 17(2), 233-267. Search in Google Scholar

[2] Bajaj, R., & Sharma, V. (2018). Smart Education with artificial intelligence based determination of learning styles. Procedia computer science, 132, 834-842. Search in Google Scholar

[3] Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. Search in Google Scholar

[4] Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312-324. Search in Google Scholar

[5] Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. Search in Google Scholar

[6] Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229-1245. Search in Google Scholar

[7] Peñalvo, F. J. G. (2023). The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or Panic?. Education in the knowledge society (EKS), (24), 1. Search in Google Scholar

[8] Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, 100082. Search in Google Scholar

[9] Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022(1), 5215722. Search in Google Scholar

[10] Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: a state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 17, 1-23. Search in Google Scholar

[11] Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. Search in Google Scholar

[12] Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., ... & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160. Search in Google Scholar

[13] Ruiz-Palmero, J., Colomo-Magaña, E., Ríos-Ariza, J. M., & Gómez-García, M. (2020). Big data in education: Perception of training advisors on its use in the educational system. social sciences, 9(4), 53. Search in Google Scholar

[14] Dwivedi, S., & Roshni, V. K. (2017, August). Recommender system for big data in education. In 2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH) (pp. 1-4). IEEE. Search in Google Scholar

[15] Santoso, L. W. (2017). Data warehouse with big data technology for higher education. Procedia Computer Science, 124, 93-99. Search in Google Scholar

[16] Attaran, M., Stark, J., & Stotler, D. (2018). Opportunities and challenges for big data analytics in US higher education: A conceptual model for implementation. Industry and Higher Education, 32(3), 169-182. Search in Google Scholar

[17] Gillborn, D., Warmington, P., & Demack, S. (2023). QuantCrit: Education, policy,’Big Data’and principles for a critical race theory of statistics. In QuantCrit (pp. 10-31). Routledge. Search in Google Scholar

[18] Dorça, F. A., Araújo, R. D., De Carvalho, V. C., Resende, D. T., & Cattelan, R. G. (2016). An Automatic and Dynamic Approach for Personalized Recommendation of Learning Objects Considering Students Learning Styles: An Experimental Analysis. Informatics in education, 15(1), 45-62. Search in Google Scholar

[19] Marienko, M., Nosenko, Y., Sukhikh, A., Tataurov, V., & Shyshkina, M. (2020). Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ education. In E3S Web of Conferences (Vol. 166, p. 10015). EDP Sciences. Search in Google Scholar

[20] Christudas, B. C. L., Kirubakaran, E., & Thangaiah, P. R. J. (2018). An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials. Telematics and Informatics, 35(3), 520-533. Search in Google Scholar

[21] Garrido, A., Morales, L., & Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications, 60, 1-15. Search in Google Scholar

[22] Raj, N. S., & Renumol, V. G. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113-148. Search in Google Scholar

[23] Liu, D. Y. T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning, 143-169. Search in Google Scholar

[24] Yu, H., Miao, C., Leung, C., & White, T. J. (2017). Towards AI-powered personalization in MOOC learning. npj Science of Learning, 2(1), 15. Search in Google Scholar

[25] Rhode, J., Richter, S., & Miller, T. (2017). Designing personalized online teaching professional development through self-assessment. TechTrends, 61, 444-451. Search in Google Scholar

[26] Nabizadeh, A. H., Leal, J. P., Rafsanjani, H. N., & Shah, R. R. (2020). Learning path personalization and recommendation methods: A survey of the state-of-the-art. Expert Systems with Applications, 159, 113596. Search in Google Scholar

[27] Zhang Suzhen,Wang Yuechun & Lv Qing.(2022).Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks.Advances in Multimedia Search in Google Scholar

[28] Marpaung F & Arnita.(2020).Comparative of prim’s and boruvka’s algorithm to solve minimum spanning tree problems.Journal of Physics: Conference Series012043-012043. Search in Google Scholar

[29] Zhang Shufan,Mao Jianlin,Wang Niya,Li Dayan & Ju Chengan.(2022).A Clustering-Enhanced Memetic Algorithm for the Quadratic Minimum Spanning Tree Problem.Entropy(1),87-87. Search in Google Scholar