College Art Education Model Based on Internet of Things and Digital Media
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Abstract
Students from a variety of fields can learn music, calligraphy, dance, theatre, cinema, and art and gain essential practical skills that essentially enhance their artistic possibilities and characteristics that foster the development of morality, intellect, physicality, and aesthetic literacy among college students in the College Art Education Model (CAEM). The aesthetic principles cultivated in CAEM are fundamental to diverse design disciplines and creative practices. The art education faces systemic challenges in pedagogical logic, particularly the misalignment between abstract artistic objectives and measurable learning outcomes, which impedes curriculum standardization and assessment validity. The Internet of Things serves as an infrastructure for continuous data acquisition and contextual awareness in college art education, enabling real-time monitoring of learning behaviors and resource utilization. Digital Media (DM) is a college art education that exceedingly effective students can be instructed in knowledge assessment, practical operation, collaboration, and lifelong learning. The CAEM-DM method proposed in this study demonstrates how digital tools can enhance the visualization of artistic elements, which is applicable to modern visual design and digital creative education. A CAEM-DM method for developing the cost-effectiveness of the innovative route in art education, the link with teachers, students, and teaching materials, and an effective model for distributing information in art teaching and extracting its potential. College students’ satisfaction with art education is up to 91% due to the study’s actual teaching activities, showing that this experiment is relatively impactful, influential, and stirs up students’ emotions. The IoT layer may be implemented through low-cost wireless sensing modules in classroom and studio environments.
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References
B. Turkcan, “Cultural Heritage Studies through Art Education: An instructional application in the ancient city of Aizanoi,” Eurasian Journal of Educational Research, vol. 19, no. 83, pp. 29-56, 2019, doi: 10.14689/ejer.2019.83.2.
F. Kong, “Evaluation model of adaptive teaching ability of college art teachers,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 9, pp. 143-155, 2020, doi: 10.3991/ijet.v15i09.14031.
N. Li, “Enhancement Strategies for Classroom Teaching Effect of Professional Art Education,” International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 6, pp. 137-153, 2021, doi: 10.3991/ijet.v16i06.21091.
X. Chen and Y. Gao, “Application and Innovation of Using Virtual Reality in Art Education,” In Proceedings of the 9th International Conference on Education and Management (ICEM 2019), 2019, doi: 10.25236/icem.2019.150.
Y. Du, “Exploration of College Art Education Based on Design Thinking,” 2019, doi: 10.25236/icem.2019.152.
L. Yang, “Research on the Application of Art Education Form in the Cultivation of College Students’ Humanistic Literacy,” 2019, doi: 10.25236/icetem.2019.136.
F. Gao, “The Application of New Media in College Art Education Teaching,” 2019, doi: 10.25236/icemeet.2019.074.
O. Malytska, I. Patron, N. Chabanenko, O. Shvets, A. Polishchuk, and L. Martyniv, “Development of Art Education as a Basis for Sustainable Development of Society,” Postmodern Openings, vol. 13, no. 1 Sup1, pp. 247-265, 2022, doi: 10.18662/po/13.1Sup1/425.
W. Ziming, “Research on intelligent data collection and quality evaluation of computer science and art education systems based on systemic multi-information fusion approach,” International Journal of Computer Applications in Technology, vol. 78, no. 1, pp. 63-71, 2026, doi: 10.1504/IJCAT.2026.151385.
L. Chen, “Simulation of university teaching achievement evaluation based on deep learning and improved vector machine algorithm,” Applied Artificial Intelligence, vol. 37, no. 1, Art. no. 2195221, 2023, doi: 10.1080/08839514.2023.2195221.
W. Zhu, “Study of creative thinking in digital media art design education,” Creative Education, vol. 11, no. 2, pp. 77-85, 2020, doi: 10.4236/ce.2020.112006.
R. Feng, T. Li, and J. Dong, “New media fine art education platform based on internet of things technology,” International Journal of Internet Protocol Technology, vol. 14, no. 3, pp. 131-138, 2021, doi: 10.1504/IJIPT.2021.117410.
R. Heaton, “Cognition in art education,” British Educational Research Journal, vol. 47, no. 5, pp. 1323-1339, 2021, doi: 10.1002/berj.3728.
X. Ling, “Model for evaluating the art education teaching quality with uncertain information,” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 1967-1972, 2019, doi: 10.3233/JIFS-179258.
G. Hongxi, “The Functional Mechanism of Art Education in College Students’ Quality Education and Aesthetic Education,” 2019, doi: 10.25236/icetem.2019.138.
Q. Liu, H. Chen, and M. Crabbe, “Interactive study of multimedia and virtual technology in art education,” International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 1, pp. 80-93, 2021, doi: 10.3991/ijet.v16i01.18227.
Z. Wen, A. Shankar, and A. Antonidoss, “Modern art education and teaching based on artificial intelligence,” Journal of Interconnection Networks, Art. no. 2141005, 2021, doi: 10.1142/S021926592141005X.
W. Mao and B. Zhang, “The Use of Digital Image Art under Visual Sensing Technology for Art Education,” Journal of Sensors, 2021, doi: 10.1155/2021/4513577.
A. Nanthanasit and N. Wongta, “Approach augmented reality real-time rendering for understanding light and shade in art education,” In 2018 International Conference on Digital Arts, Media and Technology (IC-DAMT), 2018, doi: 10.1109/ICDAMT.2018.8376498.
R. Ouyang, “Construction of online classroom instructional quality assessment system of university music based on BP neural network,” Scientific Reports, vol. 15, no. 1, Art. no. 14250, 2025, doi: 10.1038/s41598-025-98556-1.
D. Zhang, F. Wei, and H. Xie, “Application of optimization of teacher teaching path in art education based on GCN,” Journal of Engineering and Applied Science, vol. 72, no. 1, pp. 221, 2025, doi: 10.1186/s44147-025-00781-y.
B. Li, “Analysis of the Teaching Contents and Methods of Art Education in Higher Vocational Col leges Based on Computer-aided,” Journal of Physics: Conference Series, vol. 1648, no. 2, Art. no. 022009, 2020, doi: 10.1088/1742-6596/1648/2/022009.