<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of multi-dimensional preference analysis (LINMAP) to get the weight vector of attributes under the situation that the weights of attributes are completely unknown and for the uncertain multi-attribute decision-making problem that the elements of decision-making matrix are triangular fuzzy numbers, and the preference information is ordered pairs. The data of subjective or objective Civics education learning preferences are aggregated, and the statistical analysis of the data is carried out using SPSS.21 to generate the Civics education learning preference input degree weighting system. The results show that the offline behavioral input weight in the college Civics course is 0.5769, which is greater compared to the online input. And the attribute weights of course Civics preference are calculated to get <italic>Z</italic>max = 2.29, <italic>Z</italic>min = 0.73. Multiple feasible solutions can be obtained by taking any value between [0,1]. Based on the development law of the times, this paper makes a new thinking on ideological and political education and actively explores a new road suitable for the development of ideological and political education in colleges and universities.
</abstract>

This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of multi-dimensional preference analysis (LINMAP) to get the weight vector of attributes under the situation that the weights of attributes are completely unknown and for the uncertain multi-attribute decision-making problem that the elements of decision-making matrix are triangular fuzzy numbers, and the preference information is ordered pairs. The data of subjective or objective Civics education learning preferences are aggregated, and the statistical analysis of the data is carried out using SPSS.21 to generate the Civics education learning preference input degree weighting system. The results show that the offline behavioral input weight in the college Civics course is 0.5769, which is greater compared to the online input. And the attribute weights of course Civics preference are calculated to get Zmax = 2.29, Zmin = 0.73. Multiple feasible solutions can be obtained by taking any value between [0,1]. Based on the development law of the times, this paper makes a new thinking on ideological and political education and actively explores a new road suitable for the development of ideological and political education in colleges and universities.

A Study on a New Way of Thinking Based on Linear Programming to Analyze Civic Education in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of...

A Study on a New Way of Thinking Based on Linear Programming to Analyze Civic Education in Colleges and Universities

Published Online: Dec 20, 2023

Page range: -

Received: Feb 25, 2023

Accepted: Jun 30, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01569

Keywords
Attribute weight, Triangular fuzzy number, Linear programming, Multidimensional preference analysis, Multi-attribute decision-making

© 2023 Qi Tang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study on a New Way of Thinking Based on Linear Programming to Analyze Civic Education in Colleges and Universities

Published Online: Dec 20, 2023

Page range: -

Received: Feb 25, 2023

Accepted: Jun 30, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01569

KeywordsAttribute weight, Triangular fuzzy number, Linear programming, Multidimensional preference analysis, Multi-attribute decision-making

© 2023 Qi Tang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Attribute weight, Triangular fuzzy number, Linear programming, Multidimensional preference analysis, Multi-attribute decision-making