Classification of imagery from the perspective of"object and subject"and its application in the practice of aesthetic education
Innovation in aesthetic education is an important way to promote the reform and development of such education in colleges and universities in the new era.As a result,a research focus in the teaching reform of the education in institutions of higher learning is how to integrate multi-disciplinary resources based on aesthetic theory,innovate relevant educational practice,and achieve the goals of cultivating students with such education.In response,we have chosen imagery,one of the core concepts of Chinese classical aesthetics,as our focus.However,relevant researches on aesthetic education practice rooted in the"imagery"are rare and lack theoretical foundation,practical methods and case studies.In view of the lack,this paper created the two-factor imagery classification by analyzing the two key elements"object"and"subject"in the formation of imagery,from which derived five kinds of imagery relationships that are both independent and interrelated,i.e.the object itself vs.the subject human's first impression,the object with its surroundings vs.human's expression of emotions,the object with its benefits vs.human's cognition and creation in practice,the object with its connotative meanings vs.human's history and culture,and the unity of the object and the subject in artistic creations,forming a continuum of imagery.This classification was then employed to guide students' appreciation and creation of the imagery of Chinese medicinal herbs to explore the feasibility of the new educational practice for integrating aesthetic education with professional education,and improve the actual learning outcomes of such education in colleges and universities.Furthermore,our research can provide widely applicable methods for carrying out aesthetic education that meets the needs of the new era.
two factors of object and subjectclassification of imageryappreciation of Chinese medicinal herbsaesthetic education practice
国家科技期刊平台
NSTL
万方数据
