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Progress in Artificial Intelligence

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22-11-2021 | Regular Paper

Online and offline teaching connection system of college ideological and political education based on deep learning

Authors: Fenghua Qi, Yongqing Chang, K. Ramesh, P. Hemalatha

Published in: Progress in Artificial Intelligence | Issue 2/2023

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Abstract

The teaching methods demonstrate that a classical ideological and political teaching (IPT) approach pays attention to students' critical role in learning. The public teaching course is a simple mandatory course in regular schools for non-pedagogical students. It constitutes a significant course for the development of the teacher values and educational ideal of teachers. Developing an ideological and political teaching platform for teacher education in alignment with the new pedagogy and the comprehensive requirements of current ideological and political education, public education at faculty and universities focused on video-on-demand technology. This paper proposes the deep learning-based integrated educational framework (DLIEF) for online and offline teaching models for ideological and political education. The deep neural network will ultimately serve teachers' public education courses at typical universities and boost classroom teaching qualities through data compression, network technologies and database technologies. A conceptual study of the benefits and drawbacks of online and offline instruction is a sensible suggestion, and enhancement of the students' particular situation is essential to achieve more substantial ideological and cultural results in higher education colleges based on deep learning.

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Title: Online and offline teaching connection system of college ideological and political education based on deep learning
Authors: Fenghua Qi
Yongqing Chang
K. Ramesh
P. Hemalatha
Publication date: 22-11-2021
Publisher: Springer Berlin Heidelberg
Published in: Progress in Artificial Intelligence / Issue 2/2023
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-021-00268-w

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