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<p>Influenced by some traditional thinking, some college student managers do not recognize the importance of mental health education and fail to effectively integrate mental health education into student management, resulting in poor student management results. This paper proposes an improved optimization scheme for the ID3 algorithm, using the improved C4-5 algorithm to construct a decision tree model, selecting information gain and gain rate as the attribute measure of the model and deciding the split form of the sample. Succeeding pruning operations are used to correct model errors, and the rules are described in the form of TF-THEN. Mental health education is the application of the constructed data model. The effectiveness of student management after mental health education was differentially analyzed across the different identity perspectives of teachers and students. There are significant differences between teachers and students in the dimensions of practice effect, strengths evaluation, and direction of improvement, with p-values less than 0.05. In the study of the effectiveness of student management on students’ mental health, after the implementation of student management work, there are significant differences between the experimental group of students and the control group in the dimensions of self-confidence, emotional intelligence, and frustration tolerance, with a difference of 4.61, 1.6, and 7.9 points, respectively. There is a reciprocal interactive relationship between student management and mental health education.</p>
</abstract>

Influenced by some traditional thinking, some college student managers do not recognize the importance of mental health education and fail to effectively integrate mental health education into student management, resulting in poor student management results. This paper proposes an improved optimization scheme for the ID3 algorithm, using the improved C4-5 algorithm to construct a decision tree model, selecting information gain and gain rate as the attribute measure of the model and deciding the split form of the sample. Succeeding pruning operations are used to correct model errors, and the rules are described in the form of TF-THEN. Mental health education is the application of the constructed data model. The effectiveness of student management after mental health education was differentially analyzed across the different identity perspectives of teachers and students. There are significant differences between teachers and students in the dimensions of practice effect, strengths evaluation, and direction of improvement, with p-values less than 0.05. In the study of the effectiveness of student management on students’ mental health, after the implementation of student management work, there are significant differences between the experimental group of students and the control group in the dimensions of self-confidence, emotional intelligence, and frustration tolerance, with a difference of 4.61, 1.6, and 7.9 points, respectively. There is a reciprocal interactive relationship between student management and mental health education.

Modeling the Coupling of Mental Health Education and Student Management in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

Influenced by some traditional thinking, some college student managers do not recognize the importance of mental health education and fail to effectively...

Modeling the Coupling of Mental Health Education and Student Management in Colleges and Universities

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 23 kwi 2024

Przyjęty: 08 lip 2024

DOI: https://doi.org/10.2478/amns-2024-1970

Słowa kluczowe
C4-5 algorithm, Decision tree model, Information gain, Rule description, TF-THEN

© 2024 Po Yang, published by Sciendo

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

Modeling the Coupling of Mental Health Education and Student Management in Colleges and Universities

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 23 kwi 2024

Przyjęty: 08 lip 2024

DOI: https://doi.org/10.2478/amns-2024-1970

Słowa kluczoweC4-5 algorithm, Decision tree model, Information gain, Rule description, TF-THEN

© 2024 Po Yang, published by Sciendo

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

Słowa kluczowe
C4-5 algorithm, Decision tree model, Information gain, Rule description, TF-THEN