Interdisciplinary teaching strategy of multimodal data fusion in college aesthetic education curriculum using intelligence methods

Author:

Liu Fei12

Affiliation:

1. College of Fine Arts and Design, Yangzhou University, Yangzhou, Jiangsu, China

2. School of Fine Art, Jinan University, Jinan, Shandong, China

Abstract

In China, aesthetic education at the college level is essential for students’ quality because it improves their understanding of art, helps them progress in their professional career development, and helps them comprehend more fully the attractiveness of creative creations. As a result, it needs to prioritize aesthetic education at the institution and endeavor to nurture students’ feelings progressively and improve their aesthetic abilities at different levels. Artificial intelligence (AI) is used in this project to create a novel, interdisciplinary teaching technique that will maximize students’ artistic and intellectual potential and help them make more, better art. In this research, the Osprey Optimization method improves the interdisciplinary teaching technique for aesthetic education based on a light Exclusive gradient-boosting mechanism (OOM-LEGBM). The exploration-exploitation dynamics of the OOM are incorporated into LEGBM, providing the students with a tangible and relatable technique to understand complex-solving processes. This research develops an enhanced quality framework for college aesthetic education based on the multi-model data fusion system about the implication and necessity of aesthetic education. The influence of college aesthetic education on students’ creative capacity and artistic literacy was investigated to inform instructional activities better to develop students’ aesthetic skills. The experimental findings suggest that the proposed approach achieved an improved accuracy of 99.90%, higher precision of 99.88%, and greater recall of 99.91%. Moreover, it obtained a minimum Root Mean Square Error (RMSE) of 0.26% and a lower Mean Absolute Error (MAE) of 0.34%, showing that the suggested model greatly improved preference learning accuracy while keeping overall accuracy at an identical level. Innovation capacity building in college aesthetic education can help students become more self-aware, improve their study habits, visually literate, and more comprehensive.

Publisher

IOS Press

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