Nanjing University of the Arts Enhances Art Education with New Framework

News Summary

Nanjing University of the Arts has introduced an innovative emotion-driven learning analytics framework aimed at improving college art courses. The framework focuses on theoretical research, model construction, empirical analysis, and application services to optimize teaching methods and enhance student experiences. A multi-modal learning model assesses emotional responses and educational outcomes. Evaluations revealed areas for improvement in aesthetics, learning costs, and privacy, while also highlighting the importance of ethical data use. This initiative underscores the significance of emotional intelligence in art education, paving the way for a more engaging learning experience.

Nanjing University of the Arts Launches Innovative Framework to Improve Art Education

The Nanjing University of the Arts has made significant strides in enhancing college art education through the development of a new emotion-driven learning analytics framework. This framework aims to optimize teaching practices and improve the overall learning experiences of students enrolled in art courses.

Core Components of the Learning Analytics Framework

Central to this groundbreaking framework are four essential aspects: theoretical research, model construction, empirical analysis, and application services. By integrating these components, the university seeks to create a comprehensive approach to understanding and improving art education.

The resulting multi-modal learning analytics model consists of four core modules, each designed to focus on different elements of art education. These modules include emotion perception, data processing and analysis, teaching intervention, and learning outcome assessment. Together, they provide an in-depth view of how emotions influence learning in the arts.

Empirical Testing and Evaluation

Each module within the model underwent rigorous empirical testing to validate its effectiveness and optimize the learning process for both students and teachers. The evaluation methodology was anchored in the Technology Acceptance Model (TAM), assessing the framework’s applicability and user-friendliness from two perspectives: students and teachers.

Results from the evaluation indicated that the model displayed overall effectiveness. However, areas for improvement were identified, which include enhancing aspects of aesthetics, addressing learning costs, ensuring non-intrusiveness, and maintaining robust data privacy measures. These findings illustrate a commitment to refining the educational experience while prioritizing the emotional well-being of students.

Future Directions and Multidisciplinary Integration

The research also delved into future trends that could significantly impact art education. Topics of interest included multidisciplinary integration, innovations in multi-modal data collection and analysis, ensuring data security, and addressing ethical considerations related to the use of learning analytics. Such trends may pave the way for further advancements in educational practices across various disciplines.

Key Contributors and Support

This pioneering study was spearheaded by a group of distinguished researchers and artists, including contributions from an associate professor at Nanjing University of the Arts, a national artist, and another associate professor from the Department of Environmental Design at Hainan Normal University. Their collective expertise ensured a robust and well-rounded research process.

The Culture and Art Big Data Laboratory, along with the Chinese Culture Inheritance and Digital Intelligence Innovation Laboratory at Nanjing University of the Arts, played a pivotal role in supporting this research. This collaboration demonstrates a strong institutional commitment to enhancing the intersection of technology and the arts.

Transparency and Data Accessibility

Importantly, the authors of the study reported no potential conflicts of interest, enhancing the credibility of the findings. All data stemming from the study are available without restrictions, promoting a culture of transparency and collaboration within the academic community.

This innovative framework developed by the Nanjing University of the Arts marks an essential step forward in art education, with the potential to revolutionize how students interact with the subject matter. By focusing on emotions and leveraging data analytics, educators can create more engaging and effective learning environments for aspiring artists.

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Additional Resources

• Glasstire: Exchange and Dialogue – A Co-Curated China-U.S. Art Exchange
• ArchDaily: Nanjing Art Center by Studio Link-Arc
• The World of Chinese: Outside Looking In
• Encyclopedia Britannica: Eight Masters of Nanjing
• Canvas Rebel: Meet Nini Qiao