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<p>This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles through data mining techniques and applying them to student management and education to improve the accuracy and effectiveness of education management. The basic process of data mining, including data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation, was first carried out in the study. Then, a clustering recommendation algorithm based on user characteristics was designed to construct a clustering model of user interest preferences by calculating the distance between user attributes and filling the rating matrix using rating time and item type. Then, the study constructed a student user profiling system and analyzed techniques such as fuzzy C-mean clustering, association rule algorithm and user-based collaborative filtering. The study results show that applying digital health elements in student management and education helps identify potential association patterns of students’ mental health problems. For example, in the W vocational school case study, the association rule algorithm found that the sense of learning stress and compulsion were the most frequent combinations of psychological problems among students, with support levels of 0.7451 and 0.6518, respectively. In addition, the study evaluated the effects of educational management interventions on the mental health of higher vocational students, and found that, after adopting student user profiles for educational management interventions, the experimental class students’ mental health scores for each variable were 0.602 to 1.113 points higher than those of the control class, which significantly improved the students’ psychological quality.</p>
</abstract>

This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles through data mining techniques and applying them to student management and education to improve the accuracy and effectiveness of education management. The basic process of data mining, including data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation, was first carried out in the study. Then, a clustering recommendation algorithm based on user characteristics was designed to construct a clustering model of user interest preferences by calculating the distance between user attributes and filling the rating matrix using rating time and item type. Then, the study constructed a student user profiling system and analyzed techniques such as fuzzy C-mean clustering, association rule algorithm and user-based collaborative filtering. The study results show that applying digital health elements in student management and education helps identify potential association patterns of students’ mental health problems. For example, in the W vocational school case study, the association rule algorithm found that the sense of learning stress and compulsion were the most frequent combinations of psychological problems among students, with support levels of 0.7451 and 0.6518, respectively. In addition, the study evaluated the effects of educational management interventions on the mental health of higher vocational students, and found that, after adopting student user profiles for educational management interventions, the experimental class students’ mental health scores for each variable were 0.602 to 1.113 points higher than those of the control class, which significantly improved the students’ psychological quality.

Mining and Application of Digital Health Elements in Higher Education Student Management and Education

Applied Mathematics and Nonlinear Sciences

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

This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles...

Mining and Application of Digital Health Elements in Higher Education Student Management and Education

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 21. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0599

Schlüsselwörter
Data mining, User portrait, Clustering recommendation, Digital health elements, Student management

© 2024 Haibo Yu, published by Sciendo

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

Mining and Application of Digital Health Elements in Higher Education Student Management and Education

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 21. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0599

SchlüsselwörterData mining, User portrait, Clustering recommendation, Digital health elements, Student management

© 2024 Haibo Yu, published by Sciendo

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

Schlüsselwörter
Data mining, User portrait, Clustering recommendation, Digital health elements, Student management