Keywords
creative engagement
arts-based classroom
digital portfolio
process evaluation
natural language processing
student participation
Abstract
This study develops a multimodal learning analytics model to measure creative engagement in arts-based classrooms through process-oriented and quantifiable indicators. The study is designed to collect data from approximately 420 students in 20 arts-based classes over a 16-week semester, including 6,000 classroom interaction records, 3,500 verbal participation entries, 2,800 digital portfolio submissions, 1,200 peer-feedback records, and 840 reflective journals. Based on these data, the study constructs a Creative Engagement Index covering five dimensions: attention, experimentation, persistence, collaboration, and self-expression. Specific variables include attendance rate, task completion rate, revision frequency, peer interaction frequency, verbal contribution count, portfolio update interval, reflective text length, and teacher-rated engagement score. The research applies exploratory factor analysis, confirmatory factor analysis, cluster analysis, multiple regression, and natural language processing to examine how different forms of classroom behavior predict creative engagement. It also compares teacher evaluation scores with analytics-based engagement scores to test whether multimodal data can provide a more stable and transparent assessment of creative participation. The innovation of this study lies in transforming creative engagement from a subjective classroom impression into a multidimensional and data-supported measurement framework, offering a practical method for evaluating learning processes in arts-based education.
References
Zhang, Y., Gu, W., & Wang, J. (2025, December). Construction of Wind Farm Asset Health Index Based on Multi-Dimensional Indicators and Analytic Hierarchy Process and Its Correlation with Operational Performance. In Proceedings of the 2025 International Conference on Digital Transformation and Management (pp. 317-323).
Maras, K., Kriss, K., Cumming, T. M., & Hoenig, J. F. (2025). Engaging in a community of practice in visual arts: A systematic literature review. International Journal of Educational Research, 133, 102752.
Lin, T., Spivack, Z., & Chen, A. (2026). Monitoring Teaching Quality and Validating Educational Equity in Public Art Education. Available at SSRN 6512778.
Sudaryati, S. (2026). Art, Emotion, and Learning: Exploring Classroom Experiences through Creative Expression. Tinombala: Journal of Social Sciences and Education, 1(1), 12-23.
Huang, R. (2026). Less Is More: When Data Augmentation Hurts Small CNN Generalization. Journal of Computer and Communications, 14(7), 1-11.
Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
Clark-Fookes, T. (2025). Arts education’s wicked problem: tensions between reliability and validity in arts assessment. Arts Education Policy Review, 1-15.
Qi, C., & Qiao, X. (2026, May). From Traditional Machine Learning to Large Language Models: The Evolution of Infrastructure from an Engineering Perspective. In 2026 6th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 503-506). IEEE.
Su, D., & Wu, Y. (2026). Multilevel Growth and Social Network Modeling for Functional Communication Enhancement in Students with Intellectual Disabilities.
Guerrero-Sosa, J. D., Romero, F. P., Menéndez-Domínguez, V. H., Serrano-Guerrero, J., Montoro-Montarroso, A., & Olivas, J. A. (2025). A comprehensive review of multimodal analysis in education. Applied Sciences, 15(11), 5896.
Gao, G., Gao, R., Lu, C., Gao, R., & Kuang, Y. (2026, March). Security Governance Methods and Quantitative Evaluation for Enterprise SMS and Verification Code Systems. In 2026 International Conference on Generative Artificial Intelligence and Information Security (GAIIS) (pp. 455-458). IEEE.
Beldar, P., Rakhade, R., Bahiram, M., & Kadbhane, S. (2025). Innovative Coding Teaching Methodologies: A Comprehensive Approach for Diverse Learners. Journal of Engineering Education Transformations, 52-65.
Xu, T., Zhang, J., & Zhu, W. (2026). Reproducible Modeling Pipelines and Cross-Window Stability in Subprime Auto Loan Credit Risk Assessment. Available at SSRN 6893861.
Becerra, A., Cobos, R., & Lang, C. (2026). Enhancing online learning by integrating biosensors and multimodal learning analytics for detecting and predicting student behaviour: a review. Behaviour & Information Technology, 45(9), 1885-1910.
Xiong, W., & Guo, Y. (2026). A Study on the Phenomenon of Abnormal Load Concentration and Its Cross-system Transmission Characteristics in High-density Building Operations. Available at SSRN 7120661.
Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
Sattar, G., Rustemova, S., & Nebessayeva, Z. (2025). Socially Interactive Approaches and Digital Technologies in Art Education: Developing Creative Thinking in Students During Art Classes. Open Education Studies, 7(1), 20250096.
Zhang, G., Xu, Y., Zhang, M., Wang, S., & Lin, N. (2021). The brain network in support of social semantic accumulation. Social cognitive and affective neuroscience, 16(4), 393-405.
Hong, Z., Chen, H., & Xu, T. (2026). Real-Time Capacity Risk Forecasting for Low-Latency Financial Trading Cloud Platforms. Available at SSRN 7118180.
Culbreth, D., & Martin, F. (2025). Exploring the role of synchrony in asynchronous, synchronous, and quasi-synchronous online learner engagement. Educational technology research and development, 73(4), 2081-2111.
Huang, J., Xu, T., Yang, J., & Yin, J. (2026). A Graph Neural Network Approach for Financial Data Validation and Risk Identification in Large-Scale Intercompany Transactions. Available at SSRN 7175058.
Jozwik, S., O’Quin, C. B., & Steele, J. C. (2026). Developing Individualized Education Programs for Bidialectal Students With Learning Disabilities. Intervention in School and Clinic, 10534512261446605.
Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.
Yao, G., Zheng, J., Wang, Z., Zhang, W., Han, R., Zhao, C., ... & Liu, R. (2026, March). V-pruner: A fast and globally-informed token pruning framework for vision transformer. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 40, pp. 34396-34404).
Johnston, L. J., Griffin, J. E., Manolopoulou, I., & Jendoubi, T. (2025). A real-time metric of online engagement monitoring. arXiv Preprint arXiv: 2507.12162.
Sargent, P. V., Hilliger, I., & Baier, J. A. (2026). Beyond Time on Task: A Novel Analytical Framework for Assessing Student Workload and Its Relationship with Learning. Journal of Learning Analytics, 13(1), 110-129.
Lin, T., Chen, A., & Spivack, Z. (2026). Research on Collaborative Piano Teaching Models and Teaching Resource Efficiency Optimization in Public Art Education Systems. Available at SSRN 6284818.
Huang, J., Xu, T., Yang, J., & Yin, J. (2026). Blockage Early Warning and Financial Impact Quantification in the Monthly Closing Process of Fintech Companies. Available at SSRN 7179198.
DeLuca, C., Holden, M., & Rickey, N. (2025). From challenge to innovation: A grassroots study of teachers’ classroom assessment innovations. British Educational Research Journal, 51(1), 93-114.