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

<p>This paper utilizes a correlation-based smart prediction model to construct a smart teaching model for mental health. In the ABaisSvd model, the GD algorithm is used to iteratively update the matrix and optimize it by minimizing the error between the reconstructed achievement matrix and the original achievement matrix. The potential impact of attendance behavior and bias factors on grade prediction was also considered, and the prediction function was implemented and optimized after analyzing the correlation between student grades, attendance behavior, and bias factors through dimensionality reduction. According to the analysis of smart psychological teaching practices, 8.1% and 0.65% of students at University X experienced moderate and severe psychological problems, respectively. The smart classroom achieved a score of more than 87 points for the dimensions of appropriate use of educational techniques and obvious educational effects. The level of smart classroom and mental health in Group I was significantly higher than before the experimental intervention. There was no significant change in Group II before and after the intervention. Still, the level of mental health after the intervention (1.75) was also slightly higher than that before the intervention (1.74), and more than half of the students got a sense of theoretical, practical, and emotional gain in wisdom teaching. To sum up, the wisdom education model in this paper has the potential to significantly improve the mental health of college students.</p>
</abstract>

This paper utilizes a correlation-based smart prediction model to construct a smart teaching model for mental health. In the ABaisSvd model, the GD algorithm is used to iteratively update the matrix and optimize it by minimizing the error between the reconstructed achievement matrix and the original achievement matrix. The potential impact of attendance behavior and bias factors on grade prediction was also considered, and the prediction function was implemented and optimized after analyzing the correlation between student grades, attendance behavior, and bias factors through dimensionality reduction. According to the analysis of smart psychological teaching practices, 8.1% and 0.65% of students at University X experienced moderate and severe psychological problems, respectively. The smart classroom achieved a score of more than 87 points for the dimensions of appropriate use of educational techniques and obvious educational effects. The level of smart classroom and mental health in Group I was significantly higher than before the experimental intervention. There was no significant change in Group II before and after the intervention. Still, the level of mental health after the intervention (1.75) was also slightly higher than that before the intervention (1.74), and more than half of the students got a sense of theoretical, practical, and emotional gain in wisdom teaching. To sum up, the wisdom education model in this paper has the potential to significantly improve the mental health of college students.

Analysis of the Advancement and Practice of Smart Education Models on Adolescent Physical and Mental Health Education

Applied Mathematics and Nonlinear Sciences

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

This paper utilizes a correlation-based smart prediction model to construct a smart teaching model for mental health. In the ABaisSvd model, the GD algorithm...

Analysis of the Advancement and Practice of Smart Education Models on Adolescent Physical and Mental Health Education

Data publikacji: 30 maj 2024

Zakres stron: -

Otrzymano: 19 lut 2024

Przyjęty: 22 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1217

Słowa kluczowe
Smart teaching, Collaborative filtering, GD algorithm, Achievement matrix

© 2024 Yongjun Zhao et al., published by Sciendo

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

Analysis of the Advancement and Practice of Smart Education Models on Adolescent Physical and Mental Health Education

Data publikacji: 30 maj 2024

Zakres stron: -

Otrzymano: 19 lut 2024

Przyjęty: 22 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1217

Słowa kluczoweSmart teaching, Collaborative filtering, GD algorithm, Achievement matrix

© 2024 Yongjun Zhao et al., published by Sciendo

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

Słowa kluczowe
Smart teaching, Collaborative filtering, GD algorithm, Achievement matrix