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<p>In the context of big data’s growing influence on education, our study presents a novel approach to managing higher vocational student data through a model based on the “three-round education” philosophy. We construct a predictive model to dissect and categorize student performance at X higher vocational college by integrating K-prototypes and LSSVM algorithms. Our findings reveal three primary groups: high achievers (43.65%), average performers (23.38%), and those with challenges (32.97%), each showing apparent differences in academic success indicators. Impressively, the model forecasts student enrollment numbers with less than 1.077% error, providing a reliable tool for educational administrators to make informed decisions and tailor student management strategies effectively.</p>
</abstract>

In the context of big data’s growing influence on education, our study presents a novel approach to managing higher vocational student data through a model based on the “three-round education” philosophy. We construct a predictive model to dissect and categorize student performance at X higher vocational college by integrating K-prototypes and LSSVM algorithms. Our findings reveal three primary groups: high achievers (43.65%), average performers (23.38%), and those with challenges (32.97%), each showing apparent differences in academic success indicators. Impressively, the model forecasts student enrollment numbers with less than 1.077% error, providing a reliable tool for educational administrators to make informed decisions and tailor student management strategies effectively.

Integration of higher education student management and pedagogical concepts based on data-based decision making

Applied Mathematics and Nonlinear Sciences

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

In the context of big data’s growing influence on education, our study presents a novel approach to managing higher vocational student data through a model...

Integration of higher education student management and pedagogical concepts based on data-based decision making

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 29 mar 2024

Przyjęty: 17 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0991

Słowa kluczowe
K-prototypes algorithm, LS-SVM, Student clustering, Data prediction, Student management decisions

© 2024 Jun Wu et al., published by Sciendo

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

Integration of higher education student management and pedagogical concepts based on data-based decision making

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 29 mar 2024

Przyjęty: 17 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0991

Słowa kluczoweK-prototypes algorithm, LS-SVM, Student clustering, Data prediction, Student management decisions

© 2024 Jun Wu et al., published by Sciendo

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

Słowa kluczowe
K-prototypes algorithm, LS-SVM, Student clustering, Data prediction, Student management decisions