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<p>To better help college students establish correct scientific consumption concepts, this paper constructs an analysis model of college students’ consumption views based on the AdaBoost model in augmented learning. Firstly, the consumption data of college students are input into the analysis model and analyzed, and the weights of each sample are initialized. Next, a suitable weak regression model is selected, the corresponding number of iterations is determined, and the current sample weight distribution is updated according to the weak regressor weight coefficients. Using root mean square error adjustment and determining the threshold value, iterative operations are performed. Finally, a strong regression model is derived from analyzing the main factors influencing college students’ consumption perceptions. To verify that the proposed optimization path can help college students establish correct consumption concepts, simulation experiments are designed in this paper. The results show that after relying on peer education to penetrate scientific consumption in the context of Civic Education, some students’ remaining monthly living expenses increased from 300 yuan to 500 yuan. After strengthening self-education to encourage the practice of scientific consumption, the percentage of monthly living expenses stored by male students increased from 5% to 16%. Thus, based on the AdaBoost model, we can derive the factors that affect college students’ consumption to optimize the education path of college students’ consumption concept in a targeted way.</p>
</abstract>

To better help college students establish correct scientific consumption concepts, this paper constructs an analysis model of college students’ consumption views based on the AdaBoost model in augmented learning. Firstly, the consumption data of college students are input into the analysis model and analyzed, and the weights of each sample are initialized. Next, a suitable weak regression model is selected, the corresponding number of iterations is determined, and the current sample weight distribution is updated according to the weak regressor weight coefficients. Using root mean square error adjustment and determining the threshold value, iterative operations are performed. Finally, a strong regression model is derived from analyzing the main factors influencing college students’ consumption perceptions. To verify that the proposed optimization path can help college students establish correct consumption concepts, simulation experiments are designed in this paper. The results show that after relying on peer education to penetrate scientific consumption in the context of Civic Education, some students’ remaining monthly living expenses increased from 300 yuan to 500 yuan. After strengthening self-education to encourage the practice of scientific consumption, the percentage of monthly living expenses stored by male students increased from 5% to 16%. Thus, based on the AdaBoost model, we can derive the factors that affect college students’ consumption to optimize the education path of college students’ consumption concept in a targeted way.

Under the background of ideological and political education, the path optimization of college students’ consumption outlook education based on AdaBoost model

Applied Mathematics and Nonlinear Sciences

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

To better help college students establish correct scientific consumption concepts, this paper constructs an analysis model of college students’ consumption...

Under the background of ideological and political education, the path optimization of college students’ consumption outlook education based on AdaBoost model

Data publikacji: 19 lip 2023

Zakres stron: -

Otrzymano: 11 kwi 2022

Przyjęty: 26 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00059

Słowa kluczowe
AdaBoost model, Weak regressor, Weight initialization, Root mean square error, Iterative operations

© 2023 Lili Liu et al., published by Sciendo

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

Under the background of ideological and political education, the path optimization of college students’ consumption outlook education based on AdaBoost model

Data publikacji: 19 lip 2023

Zakres stron: -

Otrzymano: 11 kwi 2022

Przyjęty: 26 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00059

Słowa kluczoweAdaBoost model, Weak regressor, Weight initialization, Root mean square error, Iterative operations

© 2023 Lili Liu et al., published by Sciendo

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

Słowa kluczowe
AdaBoost model, Weak regressor, Weight initialization, Root mean square error, Iterative operations