Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

May 22, 2024


Pages


DOI

Article

Exploring the Feasibility of Integrating Marxist Philosophy into Teaching and Learning Reform in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

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Authors

Jiyun Wang Affiliation:
School of Marxism, Fuyang Normal University, Fuyang, Anhui, 236000, China.


Abstract

This paper centers on the research of integrating the teaching reform of Marxist philosophy. The study first constructs the architecture of disciplinary knowledge mapping, focusing on the design of entity recognition based on the BiLSTM CRF model and the joint entity relationship extraction model based on annotation strategy. Then, Marxist philosophy is incorporated into the construction of disciplinary knowledge ontology. Finally, the discipline-specific course corpus and data layer are constructed to explore the feasibility and nature of integrating Marxist philosophy into discipline knowledge mapping for teaching reform. Feedback survey on the four dimensions of Marxist philosophy from the cognitive, affective, and attitudinal aspects, the difference between the mean and standard deviation of the data of the two classes is slight. The significance of the four dimensions (two-tailed) is more significant than 0.05, and this study is generally applicable to the teaching reform of colleges and universities. After the teaching reform, students’ online learning behaviors increased, and test scores improved significantly, i.e., The teaching reform in colleges and universities integrating Marxist philosophy is feasible and effective.


Keywords

Disciplinary knowledge mapping, Entity-relationship extraction, CRF model, Bi-LSTM network, Teaching reforms, 97P10


Citation

Wang, J. (2024). Exploring the feasibility of integrating marxist philosophy into teaching and learning reform in colleges and universities under the architecture of disciplinary knowledge mapping. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1191

Published by: Engineering Journals

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