Accès libre

The classroom interaction and learning effect are enhanced by the intelligent teaching system in the high-quality development of colleges and universities

  
27 nov. 2024
À propos de cet article

Citez
Télécharger la couverture

Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International journal of artificial intelligence in education, 26, 771-783. Search in Google Scholar

Malik, G., Tayal, D. K., & Vij, S. (2019). An analysis of the role of artificial intelligence in education and teaching. In Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1 (pp. 407-417). Springer Singapore. Search in Google Scholar

Gocen, A., & Aydemir, F. (2020). Artificial intelligence in education and schools. Research on Education and Media, 12(1), 13-21. Search in Google Scholar

De-kun, J., & Memon, F. H. (2022). Design of mobile intelligent evaluation algorithm in physical education teaching. Mobile Networks and Applications, 1-8. Search in Google Scholar

Marković, M., Kostić Kovačević, I., Nikolić, O., & Nikolić, B. (2015). INSOS—educational system for teaching intelligent systems. Computer Applications in Engineering Education, 23(2), 268-276. Search in Google Scholar

Di, W., Danxia, X., & Chun, L. (2019). The effects of learner factors on higher-order thinking in the smart classroom environment. Journal of Computers in Education, 6(4), 483-498. Search in Google Scholar

Wu, L. (2020). Student model construction of intelligent teaching system based on Bayesian network. Personal and Ubiquitous Computing, 24(3), 419-428. Search in Google Scholar

Zou, L. (2022). An Intelligent Improvement Method Of Classroom Cognitive Efficiency Based On Multidimensional Interactive Devices. Journal of Applied Science and Engineering, 26(3), 445-454. Search in Google Scholar

Koole, T. (2015). Classroom interaction. In International encyclopedia of language and social interaction. Wiley-Blackwell. Search in Google Scholar

Rudsberg, K., Östman, L., & Aaro Östman, E. (2017). Students’ meaning making in classroom discussions: the importance of peer interaction. Cultural Studies of Science Education, 12, 709-738. Search in Google Scholar

Gardner, R. (2019). Classroom interaction research: The state of the art. Research on language and social interaction, 52(3), 212-226. Search in Google Scholar

Azhari, A. S., Priono, P., & Nuriadi, N. (2018). Speech acts of classroom interaction. International journal of linguistics, literature and culture, 4(2), 24-45. Search in Google Scholar

Solheim, K., Ertesvåg, S. K., & Dalhaug Berg, G. (2018). How teachers can improve their classroom interaction with students: New findings from teachers themselves. Journal of Educational Change, 19(4), 511-538. Search in Google Scholar

Solheim, K. (2019). Teachers’ aspirations to improve their classroom interaction. International Journal of Learning, Teaching and Educational Research, 18(6), 147-169. Search in Google Scholar

Early, D. M., Maxwell, K. L., Ponder, B. D., & Pan, Y. (2017). Improving teacher-child interactions: A randomized controlled trial of Making the Most of Classroom Interactions and My Teaching Partner professional development models. Early childhood research quarterly, 38, 57-70. Search in Google Scholar

Chen, J., & Lu, H. (2022). Evaluation method of classroom teaching effect under intelligent teaching mode. Mobile Networks and Applications, 27(3), 1262-1270. Search in Google Scholar

Li, G., Guo, X., & Xu, X. (2021, August). The Construction the Intelligent Classroom Teaching Mode based on Effective Teaching Interaction. In 2021 16th International Conference on Computer Science & Education (ICCSE) (pp. 1035-1039). IEEE. Search in Google Scholar

Wang, S., Wang, H., Jiang, Y., Li, P., & Yang, W. (2023). Understanding students’ participation of intelligent teaching: an empirical study considering artificial intelligence usefulness, interactive reward, satisfaction, university support and enjoyment. Interactive Learning Environments, 31(9), 5633-5649. Search in Google Scholar

Zhan, Z., Wu, Q., Lin, Z., & Cai, J. (2021). Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis. Australasian Journal of Educational Technology, 37(2), 96-109. Search in Google Scholar

Mohamed, H., & Lamia, M. (2018). Implementing flipped classroom that used an intelligent tutoring system into learning process. Computers & Education, 124, 62-76. Search in Google Scholar

Liang, X., Wang, S., & Cao, Q. (2021). A systematic review on Research on Teaching Interaction in the Environment of Smart Classroom. Scientific Journal of Intelligent Systems Research Volume, 3(6). Search in Google Scholar

Shi, Y., Peng, C., Wang, S., & Yang, H. H. (2018). The effects of smart classroom-based instruction on college students’ learning engagement and internet self-efficacy. In Blended Learning. Enhancing Learning Success: 11th International Conference, ICBL 2018, Osaka, Japan, July 31-August 2, 2018, Proceedings 11 (pp. 263-274). Springer International Publishing. Search in Google Scholar

He, W., Zhang, C. H., & Wu, Y. B. (2021). Design of multimedia intelligent classroom interactive teaching system based on internet of things technology. In e-Learning, e-Education, and Online Training: 7th EAI International Conference, eLEOT 2021, Xinxiang, China, June 20-21, 2021, Proceedings Part I 7 (pp. 442-452). Springer International Publishing. Search in Google Scholar

Deng, H., Du, Y., Cai, Q., & Yang, Y. (2023). Influence of Intelligent Technology Applications on the Learning Effect: Virtual Reality as an Example. International Journal of Emerging Technologies in Learning (Online), 18(10), 4. Search in Google Scholar

Danaparamita Muhammad & Gaol Ford Lumban. (2014). Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level.International Journal of Multimedia and Ubiquitous Engineering(4),109-120. Search in Google Scholar

Lan Wu. (2019). Student model construction of intelligent teaching system based on Bayesian network. Personal and Ubiquitous Computing(3),1-10. Search in Google Scholar

Choo-Yee Ting & Somnuk Phon-Amnuaisuk. (2012). Properties of Bayesian student model for INQPRO. Applied Intelligence(2),391-406. Search in Google Scholar