Teaching Analysis for Visual Communication Design with the Perspective of Digital Technology

Area of Science:

• Visual Communication Design Education
• Artificial Intelligence in Education
• Educational Technology

Background:

• Contemporary visual culture has expanded the scope of Visual Communication Design (VCD) education, necessitating advancements in teaching methods and evaluation systems.
• Digital technology offers new platforms for VCD instruction, with future integration expected to transform traditional learning approaches.
• Developing a fairer and more inclusive college art education system requires a robust VCD teaching evaluation system.

Purpose of the Study:

• To apply artificial intelligence (AI) technology to create a scientific and reliable teaching quality evaluation (TQE) system for VCD courses.
• To analyze the background, significance, and research status of TQE, genetic algorithms, and neural networks in educational contexts.
• To design and implement a TQE model that improves upon existing evaluation methods.

Main Methods:

• Systematic review of domestic and international research on TQE, genetic algorithms, and neural networks.
• Development of a TQE system for VCD using an adaptive mutation evolutionary method.
• Construction of a Backpropagation Neural Network (BPNN) model optimized by a genetic algorithm.

Main Results:

• The adaptive mutation genetic algorithm demonstrated significantly faster convergence speed compared to standard genetic algorithms.
• The optimized neural network model exhibited superior performance and faster convergence times.
• The developed TQE model achieved enhanced prediction accuracy for evaluating VCD course quality.

Conclusions:

• AI, specifically through optimized genetic algorithms and BPNN, can significantly improve the efficiency and accuracy of TQE in VCD education.
• The adaptive mutation evolutionary method offers a more effective approach to optimizing neural networks for educational evaluation.
• This research provides a robust framework for a fairer and more inclusive VCD teaching evaluation system, aligning with the evolving landscape of art education.