AEPH
Home > Higher Education and Practice > Vol. 1 No. 9 (HEP 2024) >
Application of Artificial Intelligence in Personalized Teaching: Taking the “Python Programming Design” Course as an Example
DOI: https://doi.org/10.62381/H241905
Author(s)
Jianmei Chen, Xiaojun Ding*
Affiliation(s)
School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, China *Corresponding Author.
Abstract
Taking the course of "Python Programming Design" as an example, this paper explores the personalized teaching of Artificial Intelligence. In the teaching practice, with Student A demonstrating the design of questions, the difficulty is gradually increased according to the learning progress. The case analysis involves the application methods, effect evaluation, feedback from teachers and students, etc. The paper also elaborates on the design practice and effect evaluation methods of Artificial Intelligence teaching in the course. The conclusion points out its progress, problems and optimization strategies. Although the research has limitations, the development trend of Artificial Intelligence teaching is promising. In the future, multiple technologies can be integrated and interdisciplinary teaching can be carried out.
Keywords
Artificial Intelligence; Personalized Teaching; Python Programming Design
References
[1].Van der Vorst T and N Jelicic. Artificial Intelligence in Education: Can AI bring the full potential of personalized learning to education? 30th European Conference of the International Telecommunications Society: "Towards a Connected and Automated Society". 2019. [2].Brusilovsky P, Malmi L, Hosseini R, et al. An integrated practice system for learning programming in Python: design and evaluation. Research and practice in technology enhanced learning, 2018. 13: p.1-40. [3].Orr JW and N Russell. Automatic assessment of the design quality of python programs with personalized feedback. arXiv preprint arXiv:2106.01399, 2021.p.1-8. [4].Holsapple K and AC Bart. Designing Designer: The Evidence-Oriented Design Process of a Pedagogical Interactive Graphics Python Library. in Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, 2022.1: p. 85-91. [5].Hashim S, Omar MK, Ab Jalil H, et al. Trends on technologies and artificial intelligence in education for personalized learning: systematic literature. Journal of Academic Research in Progressive Education and Development, 2022. 12(1): p. 884-903. [6].Bhutoria A. Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 2022. 3: p. 100068. [7].Al-Badi A and A Khan. Perceptions of learners and instructors towards artificial intelligence in personalized learning. Procedia computer science, 2022. 201: p. 445-451. [8].Rivers C and A Holland. Management education and artificial intelligence: Toward personalized learning, in The future of management education. Routledge, 2022. p. 184-204. [9].Pratama MP, R Sampelolo and H Lura. Revolutionizing education: harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of education, language teaching and science, 2023. 5(2): p. 350-357. [10].Kazemitabaar M, Chyhir V, Weintrop D, et al. Codestruct: Design and evaluation of an intermediary programming environment for novices to transition from scratch to python. in Proceedings of the 21st Annual ACM Interaction Design and Children Conference, 2022. p. 261-73 [11].Wang L, Sy A, Liu L, et al. Deep knowledge tracing on programming exercises. in Proceedings of the fourth (2017) ACM conference on learning@ scale, 2017. p. 201-04. [12].Lin P and S Chen. Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 2020. 8: p. 45689-45699. [13].McNamara DS, SA Crossley and R Roscoe. Natural language processing in an intelligent writing strategy tutoring system. Behavior research methods, 2013. 45: p. 499-515. [14].Bagunaid W, N Chilamkurti and P Veeraraghavan. Aisar: Artificial intelligence-based student assessment and recommendation system for e-learning in big data. Sustainability, 2022. 14(17): p. 10551. [15].Dhananjaya GM, Goudar RH, Kulkarni A, et al. A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review. IEEE Access, 2024. 12. p. 34019–34041
Copyright @ 2020-2035 Academic Education Publishing House All Rights Reserved