Research on Marxist Classics Education based on Deep Learning under e-Education

DOI

10.2991/978-94-6463-600-0_43How to use a DOI?

Keywords

Deep Learning; Marxist Classical Education; E-Education; Natural Language Processing

Abstract

In the digital era, traditional Marxist education methods, which rely heavily on classroom lectures and textbook reading, lack engagement and fail to integrate modern technology, resulting in limited effectiveness and low student interest. This paper aims to address these gaps by proposing a deep learning-based model that leverages advanced natural language processing and personalized recommendation techniques to enhance the effectiveness of Marxist classical education. The advent of deep learning technology has opened up new avenues for the delivery of personalised and intelligent educational content. This paper puts forth an innovative proposal for a Marxist classical education model based on deep learning. The model employs natural language processing technology to conduct comprehensive analysis of Marxist classic texts, construct knowledge graphs, and extract pivotal concepts and themes. By training a language model based on the Transformer architecture, the system is capable of automatically generating text summaries, answering students’ questions, and providing personalised learning suggestions. Additionally, an intelligent recommendation system is integrated to enable the dynamic customisation of learning paths according to students’ learning behaviours and interests, thereby enhancing the efficiency and effectiveness of the learning process. The proposed deep learning-based model not only improves student engagement and comprehension but also provides valuable insights for educators and policymakers in modernizing the curriculum of Marxist education. By integrating intelligent recommendation systems, the model facilitates a personalized and dynamic learning experience, which could lead to a paradigm shift in teaching strategies and curriculum design, making Marxist education more relevant and accessible in the digital era.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yige Qiao
PY  - 2024
DA  - 2024/12/13
TI  - Research on Marxist Classics Education based on Deep Learning under e-Education
BT  - Proceedings of the 4th International Conference on New Media Development and Modernized Education (NMDME 2024)
PB  - Atlantis Press
SP  - 377
EP  - 383
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-600-0_43
DO  - 10.2991/978-94-6463-600-0_43
ID  - Qiao2024
ER  -