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

<p>With the rapid development of information technology, the development of information technology has become the main focus of national development, and the informationization of education in colleges and universities has also been mentioned as a strategic height, and various fields such as student management and teaching methods are also being continuously intelligentized and informative. In this paper, we obtain relevant data from the information systems of colleges and universities and use the K-Means clustering algorithm based on information entropy and density optimization to cluster and analyze educational data. The optimal clustering coefficient is used to determine major school behavioral characteristics, such as students’ academic performance, consumption amount, access control records, and so forth. It can be analyzed. Are respectively used as the clustering dimensions of K-Means, and the information entropy is used to determine the attribute weight values to classify these dimensions, and to find out the common prominent characteristics in their same class. Then analyze the laws of student behavior and cluster them into categories. The academic early warning model evaluation index is used to verify the model’s accuracy. Among them, the academic early warning model proposed in this paper has an accuracy rate of 95.2%, while the precision rate and recall rate at different warning levels reach more than 90%. The validity of the model has been verified. The study shows that the introduction of educational data mining technology can solve the pain points for modeling and analyzing diverse data and accurately predict student behavior.</p>
</abstract>

With the rapid development of information technology, the development of information technology has become the main focus of national development, and the informationization of education in colleges and universities has also been mentioned as a strategic height, and various fields such as student management and teaching methods are also being continuously intelligentized and informative. In this paper, we obtain relevant data from the information systems of colleges and universities and use the K-Means clustering algorithm based on information entropy and density optimization to cluster and analyze educational data. The optimal clustering coefficient is used to determine major school behavioral characteristics, such as students’ academic performance, consumption amount, access control records, and so forth. It can be analyzed. Are respectively used as the clustering dimensions of K-Means, and the information entropy is used to determine the attribute weight values to classify these dimensions, and to find out the common prominent characteristics in their same class. Then analyze the laws of student behavior and cluster them into categories. The academic early warning model evaluation index is used to verify the model’s accuracy. Among them, the academic early warning model proposed in this paper has an accuracy rate of 95.2%, while the precision rate and recall rate at different warning levels reach more than 90%. The validity of the model has been verified. The study shows that the introduction of educational data mining technology can solve the pain points for modeling and analyzing diverse data and accurately predict student behavior.

Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment

Applied Mathematics and Nonlinear Sciences

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

With the rapid development of information technology, the development of information technology has become the main focus of national development, and the...

Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 02 mar 2024

Accettato: 26 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1613

Parole chiave
Data mining, Clustering algorithm, Academic warning model, Informative teaching

© 2024 Liguan Chen, published by Sciendo

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

Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 02 mar 2024

Accettato: 26 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1613

Parole chiaveData mining, Clustering algorithm, Academic warning model, Informative teaching

© 2024 Liguan Chen, published by Sciendo

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

Parole chiave
Data mining, Clustering algorithm, Academic warning model, Informative teaching