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

<p>Currently, college students are facing severe employment challenges. To address this, this study uses mental health education assessment data to explore how to optimize the employment guidance and management of college students in order to enhance their employability and psychological adaptability. Decision tree algorithm and improved K-means clustering algorithm are used to analyze the mental health assessment data in depth. Data mining techniques were used to identify students’ employment tendencies and psychological characteristics so as to develop personalized employment guidance strategies. It was found that the accuracy and effectiveness of career guidance can be effectively enhanced through a data-driven approach. Specifically, the accuracy of predicting employment tendency using the decision tree model reaches 85%, while the application of the improved K-means algorithm for clustering analysis of students’ psychological characteristics enables more precise customization of employment guidance strategies. Comprehensive data analysis and psychological assessment can significantly improve the quality of employment guidance for college students. Colleges and universities should pay attention to the use of mental health assessment data, combined with data mining technology, to optimize career guidance and management, in order to help students better adapt to the workplace and enhance their employment competitiveness.</p>
</abstract>

Currently, college students are facing severe employment challenges. To address this, this study uses mental health education assessment data to explore how to optimize the employment guidance and management of college students in order to enhance their employability and psychological adaptability. Decision tree algorithm and improved K-means clustering algorithm are used to analyze the mental health assessment data in depth. Data mining techniques were used to identify students’ employment tendencies and psychological characteristics so as to develop personalized employment guidance strategies. It was found that the accuracy and effectiveness of career guidance can be effectively enhanced through a data-driven approach. Specifically, the accuracy of predicting employment tendency using the decision tree model reaches 85%, while the application of the improved K-means algorithm for clustering analysis of students’ psychological characteristics enables more precise customization of employment guidance strategies. Comprehensive data analysis and psychological assessment can significantly improve the quality of employment guidance for college students. Colleges and universities should pay attention to the use of mental health assessment data, combined with data mining technology, to optimize career guidance and management, in order to help students better adapt to the workplace and enhance their employment competitiveness.

A study on the administration of career guidance to college students in higher education based on mental health education assessment data

Applied Mathematics and Nonlinear Sciences

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

Currently, college students are facing severe employment challenges. To address this, this study uses mental health education assessment data to explore how to...

A study on the administration of career guidance to college students in higher education based on mental health education assessment data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 12. Jan. 2024

Akzeptiert: 18. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0519

Schlüsselwörter
Mental health assessment, Data mining, Decision tree model, Career guidance

© 2024 Lang Wang, published by Sciendo

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

A study on the administration of career guidance to college students in higher education based on mental health education assessment data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 12. Jan. 2024

Akzeptiert: 18. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0519

SchlüsselwörterMental health assessment, Data mining, Decision tree model, Career guidance

© 2024 Lang Wang, published by Sciendo

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

Schlüsselwörter
Mental health assessment, Data mining, Decision tree model, Career guidance