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Intelligent Analytics for Educational Big Data and Its Application to Instructional Management

Nan Zhang

Nan Zhang

Shenyang UniversityShenyang, China
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Zhang, Nan
  
03 févr. 2025
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Publié en ligne: 03 févr. 2025
Reçu: 29 août 2024
Accepté: 16 déc. 2024
DOI: https://doi.org/10.2478/amns-2025-0002
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© 2025 Nan Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

DBSCAN clustering principle
DBSCAN clustering principle

Figure 2.

Student aca demic warning model
Student aca demic warning model

Figure 3.

Student achievement and borrowing number clustering
Student achievement and borrowing number clustering

Figure 4.

Student achievement and borrowing number clustering
Student achievement and borrowing number clustering

Figure 5.

Student borrowing number and consumer data clustering
Student borrowing number and consumer data clustering

Figure 6.

The distribution of the probability of management
The distribution of the probability of management

Figure 7.

Distribution of psychological and English scores
Distribution of psychological and English scores

Figure 8.

The distribution of the design of the Java programming
The distribution of the design of the Java programming

Figure 9.

The performance distribution of the soft Java programming
The performance distribution of the soft Java programming

Figure 10.

Characteristic correlation scatter point diagram
Characteristic correlation scatter point diagram

Figure 11.

Ranking of characteristic importance
Ranking of characteristic importance

Four algorithms test results

Algorithm Classification accuracy Prediction accuracy Recall rate F1 score
XGBoost 0.96341 0.89452 0.88973 0.92106
RF 0.9418 0.8703 0.85812 0.85033
LGB 0.93752 0.88594 0.83093 0.87901
GBDT 0.91602 0.87763 0.84945 0.85132
Langue:
Anglais
Périodicité:
1 fois par an
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Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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