Deep Learning-Based Big Data Analytics Model Based on Teaching Reforms in Three-Dimensional Composition

Abstract

With the development of online education and big data analysis, new teaching models and methods have emerged. The integration of online and offline teaching modes based on big data analysis has become an effective way to promote teaching reform and practice in the field of three-dimensional composition. It is important to incorporate teaching reform into the teaching of three-dimensional composition to improve the quality of education and better prepare students for their future careers. This paper evaluated the contribution of teaching reform to the improvement of student performance. This paper designed a Deep Learning (DL) big data analytics model for data clustering and classification. The student performance is monitored for both online teaching and offline teaching classes. The collected data is clustered with the directional clustering process for the computation of feature space. With the estimated feature space value Hidden Markov Model (HMM) is implemented for the estimation of statistical data derived from the feature spaces. The extracted data were applied over the RESENT- 50 model for the classification of students’ performance. The data analysis with DL model stated that student performance in offline teaching is more significant than offline teaching in 3-dimensional aspects.