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<p>Big data technology’s quick advancement and widespread use provide fresh perspectives on how to change the mental health education curriculum at colleges and institutions. This study builds a prediction model for the college mental health education curriculum using big data technology, examines the prediction model’s development process, and builds a model assessment index using the SVM support vector machine technique. Second, it assesses the development status of the mental health education course reform in colleges and universities by looking at three elements of the mental health education courses in colleges and universities: the teaching strategy, the learning environment, and the student’s learning habits. Finally, using big data technologies, we investigated how college students’ happiness with their learning in mental health education courses was influenced by the mediating role of these courses and evaluated regression analyses of both holistic and partial teaching approaches. According to the findings, a comprehensive teaching approach had an 86.5% negative impact on student’s mental health under the direct effect, with a standard effect value of −0.134. Under the indirect impact, the standardized effect value of a holistic teaching approach on students’ mental health was −0.019, and the effect proportion was 12.7%. This demonstrates the significance of big data technologies for the establishment of a new mental health education curriculum in universities.</p>
</abstract>

Big data technology’s quick advancement and widespread use provide fresh perspectives on how to change the mental health education curriculum at colleges and institutions. This study builds a prediction model for the college mental health education curriculum using big data technology, examines the prediction model’s development process, and builds a model assessment index using the SVM support vector machine technique. Second, it assesses the development status of the mental health education course reform in colleges and universities by looking at three elements of the mental health education courses in colleges and universities: the teaching strategy, the learning environment, and the student’s learning habits. Finally, using big data technologies, we investigated how college students’ happiness with their learning in mental health education courses was influenced by the mediating role of these courses and evaluated regression analyses of both holistic and partial teaching approaches. According to the findings, a comprehensive teaching approach had an 86.5% negative impact on student’s mental health under the direct effect, with a standard effect value of −0.134. Under the indirect impact, the standardized effect value of a holistic teaching approach on students’ mental health was −0.019, and the effect proportion was 12.7%. This demonstrates the significance of big data technologies for the establishment of a new mental health education curriculum in universities.

Exploring the path of reforming college mental health education curriculum based on big data technology

Applied Mathematics and Nonlinear Sciences

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

Big data technology’s quick advancement and widespread use provide fresh perspectives on how to change the mental health education curriculum at colleges and...

Exploring the path of reforming college mental health education curriculum based on big data technology

Data publikacji: 02 paź 2023

Zakres stron: -

Otrzymano: 27 paź 2022

Przyjęty: 12 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00463

Słowa kluczowe
Predictive model, SVM support vector machine, Holistic teaching style, Mediating effect, Student satisfaction

© 2023 Lingjing Chen, published by Sciendo

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

Exploring the path of reforming college mental health education curriculum based on big data technology

Data publikacji: 02 paź 2023

Zakres stron: -

Otrzymano: 27 paź 2022

Przyjęty: 12 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00463

Słowa kluczowePredictive model, SVM support vector machine, Holistic teaching style, Mediating effect, Student satisfaction

© 2023 Lingjing Chen, published by Sciendo

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

Słowa kluczowe
Predictive model, SVM support vector machine, Holistic teaching style, Mediating effect, Student satisfaction