An Empirical Study on Big Data Model and Visualization of Internet+ Teaching
Author(s) -
Zhang Hua,
SangBing Tsai
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/9974891
Subject(s) - curriculum , context (archaeology) , the internet , dimension (graph theory) , big data , perspective (graphical) , variance (accounting) , computer science , explanatory power , mathematics education , knowledge management , psychology , pedagogy , mathematics , artificial intelligence , geography , data mining , world wide web , philosophy , accounting , archaeology , epistemology , pure mathematics , business
In this paper, we conduct an in-depth study and analysis of “Internet+ Business English” teaching through teaching big data model and visualization and elaborate on the change of educational objectives in the context of Internet education. From the perspective of individual value coordinates, the goal of education is to adhere to the people-oriented, personalized, and comprehensive free development of human beings; from the perspective of social value coordinates, the goal of education is to cultivate innovative talents for social innovation and development; through the multiple perspectives of the curriculum in the context of Internet education, we analyze the goal orientation and value reshaping of the curriculum in the context of educational change. The curriculum of the future will develop toward an intelligent curriculum and introduces the curriculum design and the form of curriculum organization in the context of Internet education. A comparison of the constructed regression and classification of a total of 27 data models reveals that the model constructed based on all data in the integrated education system is the most effective. The multiple linear regression model explained up to 66.5% of the variance in student academic performance; the explanatory power of the social and demographic characteristics dimension variables ranged from approximately 13% to 18%, the personal characteristics dimension variables ranged from 7% to 20%, and the student input dimension variables ranged from 10% to 17%. The highest correct prediction rate of the binary logistic regression model was 69%.
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