Integrating ecological philosophy into ideological and political education in universities: Bridging with biomechanics for sustainable development and human health considerations
Abstract
The ideological and political education (IPE) ecosystem in universities is not only an essential component of the broader educational ecosystem but also a microcosm of it. In an era that emphasizes ecology and sustainability, integrating the concepts of the biomechanics field into the ecological environment of IPE in universities holds significant practical implications. Biomechanics, a discipline dedicated to studying the mechanical behavior of organisms, has a profound impact on aspects such as the ecological environment and human health. From the perspective of ecological philosophy, the ecological environment of IPE in universities should fully consider the sustainable development of biomechanical technologies and their influence on human health. This paper presents a dynamic early warning system for IPE in universities based on an improved Support Vector Machine (SVM) algorithm. This system aims to optimize the IPE model and enhance educational effectiveness while incorporating concepts related to biomechanics. We have constructed an evaluation index system for the quality of IPE in universities across five dimensions: basic quality, teaching attitude, teaching method, teaching ability, and teaching effect. These indicators are evaluated through expert scoring to generate a comprehensive assessment of the teaching quality of IPE. Taking into account the close connection between human mechanical responses and the ecological environment in the field of biomechanics, during the evaluation process, we incorporate the impact of the ecological environment on the human biomechanical state (such as bone development and muscle function) into consideration. This approach guides students to establish a correct ecological view and understanding of biomechanical technologies. Model simulations and performance verifications show that the fuzzy neural network undergoes 779 training iterations, with a training target set at 0.05 and a learning rate of 0.09. This model demonstrates a strong ability to provide comprehensive dynamic early warnings for IPE in universities. It has obvious advantages in adaptability, model fitting accuracy, and processing efficiency in the face of information overload, contributing to the continuous development of IPE in universities from the integrated perspective of ecological philosophy and biomechanics.
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