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

<p>This paper designs a Civic Education model based on a physiological data identification algorithm, highlighting the training enhancement of physiological data mining Civic Education for students. In the design process, the clustering similarity is calculated based on the attribute distance, and the optimal number of clusters is selected using the contour coefficient. On the basis of determining the optimal number of clusters, the maximum number of iterations is performed, and the output at termination is used to divide the thinking and teaching ability and ability assessment index association distinction. The minimum confidence threshold association rule is developed through the candidate item support, and weighted random selection is performed for each dimension. To verify the feasibility of this educational model, its teaching effect is analyzed. The experimental results show that the accuracy of the output results is high, and the average time of Civic Education of the philosophy of dealing with the world is about 0.65 seconds. The highest psychological recall rate of Civic Education reached 0.59. It shows that physiological data mining can guide students to build a Civic Education thinking system so that the course and Civic Education can form a synergistic effect, thus forming a horizontal and vertical synergistic education situation.</p>
</abstract>

This paper designs a Civic Education model based on a physiological data identification algorithm, highlighting the training enhancement of physiological data mining Civic Education for students. In the design process, the clustering similarity is calculated based on the attribute distance, and the optimal number of clusters is selected using the contour coefficient. On the basis of determining the optimal number of clusters, the maximum number of iterations is performed, and the output at termination is used to divide the thinking and teaching ability and ability assessment index association distinction. The minimum confidence threshold association rule is developed through the candidate item support, and weighted random selection is performed for each dimension. To verify the feasibility of this educational model, its teaching effect is analyzed. The experimental results show that the accuracy of the output results is high, and the average time of Civic Education of the philosophy of dealing with the world is about 0.65 seconds. The highest psychological recall rate of Civic Education reached 0.59. It shows that physiological data mining can guide students to build a Civic Education thinking system so that the course and Civic Education can form a synergistic effect, thus forming a horizontal and vertical synergistic education situation.

Research on the Application of Civic Education into the Brain and Heart Based on Physiological Data Recognition Algorithm

Applied Mathematics and Nonlinear Sciences

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

This paper designs a Civic Education model based on a physiological data identification algorithm, highlighting the training enhancement of physiological data...

Research on the Application of Civic Education into the Brain and Heart Based on Physiological Data Recognition Algorithm

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 13. Jan. 2023

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00529

Schlüsselwörter
Physiological data mining, Clustering similarity, Contour coefficient, The maximum number of iterations

© 2023 Guanglei Zhang et al., published by Sciendo

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

Research on the Application of Civic Education into the Brain and Heart Based on Physiological Data Recognition Algorithm

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 13. Jan. 2023

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00529

SchlüsselwörterPhysiological data mining, Clustering similarity, Contour coefficient, The maximum number of iterations

© 2023 Guanglei Zhang et al., published by Sciendo

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

Schlüsselwörter
Physiological data mining, Clustering similarity, Contour coefficient, The maximum number of iterations