Innovative Teaching Strategies for Aesthetic Arts in Higher Education Institutions Based on Big Data Technology

Abstract

Aesthetic education is the right thing to do in higher education institutions to establish moral education. To accurately grasp the basic concept of educating people with beauty, effectively integrate the academic and practical teaching resources of aesthetic education, and create a system of aesthetic education in higher vocational institutions with the cultivation of aesthetic and humanistic qualities as the core and the cultivation of innovative abilities as the focus, is not only necessary new ideas teaching way aesthetic learning approach deepen the reform of aesthetic education classroom, but also necessary increase mechanism of educating people in school optimize the pattern of talent cultivation. This paper discusses the mathematical model of innovative teaching to determine the current scientific and technological period with different regions as the benchmark. Under the teaching program evaluation model, the selection of variables for the evaluation model, the single-stage and multi-stage innovative teaching approach, mutual assistance final validation research of the model. The results show that the mean values of both pre and post-class performance changed, with the experimental class performance improving at a value of 21.68 and the control class performance improving at a value of 1.52, with inconsistency between the two contrasting carriers’ performance (t=2.266, df=68, p=0.032<0.05) and the experimental class performance on the post-test being better control team. The obvious way to learn the effect of an innovative, better learning path is the most authentic. The big data teaching model constructed in this paper can provide a more complex and accurate analysis of the teaching process and realize a wider range of applications, such as learning early warning, learning intervention, learning diagnosis, learning recommendation, comprehensive literacy evaluation, and student growth monitoring, to provide students with accurate and personalized education services.

Keywords

• big data
• higher education institutions
• mathematical model
• post-test
• linear regression
• 62j05