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

<p>With the rapid development of information technology and scientific education, the work of ideological and political education in colleges and universities has also undergone important changes. In this paper, the gradient value of the learning behavior data of college ideological and political students is extracted by HOG features, and the extracted student behavior data is inputted into the learning dashboard to be analyzed, and the pedagogical feedback of ideological and political learning is obtained. Two classes, category features and numeric features, are categorized by the Kprototype algorithm for the educational dataset. Moreover, through the learning dashboard, learners’ learning-related data is graphically visualized in multiple dimensions to explore the factors that affect the effect of learning Civics and Politics in colleges and universities. Finally the teaching of college ideology and politics in University G is used as the metadata of the study to analyze the utility of the learning dashboard innovative teaching tool in teaching college ideology and politics. The results show that the effect value of the learning dashboard on the learning effect of Civics is 0.804, and the effect value on the emotion of Civics learners is 0.385, respectively, which both have a large degree of positive influence. The use of a learning dashboard can positively impact the learning behavior of Civics learners.</p>
</abstract>

With the rapid development of information technology and scientific education, the work of ideological and political education in colleges and universities has also undergone important changes. In this paper, the gradient value of the learning behavior data of college ideological and political students is extracted by HOG features, and the extracted student behavior data is inputted into the learning dashboard to be analyzed, and the pedagogical feedback of ideological and political learning is obtained. Two classes, category features and numeric features, are categorized by the Kprototype algorithm for the educational dataset. Moreover, through the learning dashboard, learners’ learning-related data is graphically visualized in multiple dimensions to explore the factors that affect the effect of learning Civics and Politics in colleges and universities. Finally the teaching of college ideology and politics in University G is used as the metadata of the study to analyze the utility of the learning dashboard innovative teaching tool in teaching college ideology and politics. The results show that the effect value of the learning dashboard on the learning effect of Civics is 0.804, and the effect value on the emotion of Civics learners is 0.385, respectively, which both have a large degree of positive influence. The use of a learning dashboard can positively impact the learning behavior of Civics learners.

Research on Teaching Characteristics of Innovative Civic and Political Education in Colleges and Universities Based on HOG Feature Extraction

Applied Mathematics and Nonlinear Sciences

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

With the rapid development of information technology and scientific education, the work of ideological and political education in colleges and universities has...

Research on Teaching Characteristics of Innovative Civic and Political Education in Colleges and Universities Based on HOG Feature Extraction

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 18. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0152

Schlüsselwörter
HOG feature extraction, Gradient value, Learning dashboard, Kprototype algorithm, Learning behavior

© 2024 Xuechun Wei, published by Sciendo

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

Research on Teaching Characteristics of Innovative Civic and Political Education in Colleges and Universities Based on HOG Feature Extraction

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 18. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0152

SchlüsselwörterHOG feature extraction, Gradient value, Learning dashboard, Kprototype algorithm, Learning behavior

© 2024 Xuechun Wei, published by Sciendo

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

Schlüsselwörter
HOG feature extraction, Gradient value, Learning dashboard, Kprototype algorithm, Learning behavior