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<p>Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and inescapable new environment and new opportunity, and the way of student management by counselors in higher education institutions is constantly developing and changing. Based on this, the use of big data to promote the innovation of counselor student management in higher education institutions is an inevitable issue to deepen the comprehensive reform in the field of education and a real issue in front of the majority of educators. In this study, the performance of the K-Means algorithm and PageRank are tested from the perspective of big data to find the most effective ways and methods to improve the counselors’ student management work, and the performance and hit rate of each strategy is tested in the application of K-Means. This means application, the LRFU strategy (<italic>step</italic> = 0) also achieved relatively good performance, using HiBench to run PageRank algorithm test cases. The results showed that the LRU strategy in the PageRank test case also improved the hit rate by 25% compared with other strategies and the hit rate by 13% compared with the LRFU strategy (<italic>step</italic> = 0). Student management efficiency. Higher education institutions can reform counselors’ student management methods by establishing a large student database management center, strengthening big data analysis, improving the ability of student data mining, processing, and management work, and striving to maximize the efficiency of student management data and information.</p>
</abstract>

Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and inescapable new environment and new opportunity, and the way of student management by counselors in higher education institutions is constantly developing and changing. Based on this, the use of big data to promote the innovation of counselor student management in higher education institutions is an inevitable issue to deepen the comprehensive reform in the field of education and a real issue in front of the majority of educators. In this study, the performance of the K-Means algorithm and PageRank are tested from the perspective of big data to find the most effective ways and methods to improve the counselors’ student management work, and the performance and hit rate of each strategy is tested in the application of K-Means. This means application, the LRFU strategy (step = 0) also achieved relatively good performance, using HiBench to run PageRank algorithm test cases. The results showed that the LRU strategy in the PageRank test case also improved the hit rate by 25% compared with other strategies and the hit rate by 13% compared with the LRFU strategy (step = 0). Student management efficiency. Higher education institutions can reform counselors’ student management methods by establishing a large student database management center, strengthening big data analysis, improving the ability of student data mining, processing, and management work, and striving to maximize the efficiency of student management data and information.

Innovative measures of student management by counselors in higher education institutions under a big data environment

Applied Mathematics and Nonlinear Sciences

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

Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and...

Innovative measures of student management by counselors in higher education institutions under a big data environment

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 06. Sept. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00171

Schlüsselwörter
Big data, Counselors in higher education institutions, LRFU strategy, K-Means algorithm, PageRank test

© 2023 Xiao Liu, published by Sciendo

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

Innovative measures of student management by counselors in higher education institutions under a big data environment

Online veröffentlicht: 11. Aug. 2023

Seitenbereich: -

Eingereicht: 06. Sept. 2022

Akzeptiert: 20. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00171

SchlüsselwörterBig data, Counselors in higher education institutions, LRFU strategy, K-Means algorithm, PageRank test

© 2023 Xiao Liu, published by Sciendo

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

Schlüsselwörter
Big data, Counselors in higher education institutions, LRFU strategy, K-Means algorithm, PageRank test