Accès libre

Data mining and neural network modeling for teaching and learning in vocational education: promoting innovation in academic management and teaching reforms

Wangkai Xu

Wangkai Xu

Ningbo PolytechnicNingbo, China
Recherchez cet auteur sur
Sciendo|Google Scholar
Xu, Wangkai
, 
Geng Rui

Geng Rui

Mudanjiang Medical UniversityMudanjiang, China
Recherchez cet auteur sur
Sciendo|Google Scholar
Rui, Geng
 et 
Lihuan Xu

Lihuan Xu

Hanshan Normal UniversityChaozhou, China
Recherchez cet auteur sur
Sciendo|Google Scholar
Xu, Lihuan
  
19 mars 2025
Data mining and neural network modeling for teaching and learning in vocational education: promoting innovation in academic management and teaching reforms's Cover Image
À propos de cet article
Article précédent
Article suivant
Citez
Télécharger la couverture
Publié en ligne: 19 mars 2025
Reçu: 22 nov. 2024
Accepté: 20 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0444
Mots clés
© 2025 Wangkai Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Data mining flow chart
Data mining flow chart

Figure 2.

The results of four professional students and their corresponding courses
The results of four professional students and their corresponding courses

Figure 3.

Characteristic correlation scatter point diagram
Characteristic correlation scatter point diagram

Figure 4.

Tree model decision case diagram
Tree model decision case diagram

Figure 5.

Neural network decision flow
Neural network decision flow

Figure 6.

TabNet schema
TabNet schema

Figure 7.

Attentive transformer Framework
Attentive transformer Framework

Figure 8.

The results of the decision tree on the warning data
The results of the decision tree on the warning data

Figure 9.

Support vector machine algorithm results in the warning data set
Support vector machine algorithm results in the warning data set

Figure 10.

The loss rate curve of the two algorithms in the training set and the test set
The loss rate curve of the two algorithms in the training set and the test set

Figure 11.

The 2 algorithms are in the training set and the roc curve on the test set
The 2 algorithms are in the training set and the roc curve on the test set

Data input format for the first three semestings

First semester
Student number(ID) 20240528 20240157 20240346 2024004
The first semester 3.72 2.56 2.88 3.83
First semester credit 27.5 32.0 28.5 27.0
No credit for the first semester 0 4.5 0 5.0
The first of the semester 0 2 0 3
First semester average 88.53 76.56 72.38 88.86
Second semester
Student number(ID) 20240077 20240172 20240369 20240605
The second semester 2.90 2.77 3.52 3.96
Second t semester credit 27.0 30.5 26.5 28.0
No credit for the second semester 4.5 0 0 5.5
The second of the semester 1 0 0 2
Second semester average 88.63 76.66 72.48 88.96
Third semester
Student number(ID) 20240077 20240172 20240369 20240605
The third semester 2.90 2.76 3.51 3.95
Third semester credit 27.0 29.5 25.5 28.0
No credit for the third semester 0 4.5 5.5 0
The third of the semester 0 1 2 0
Third semester average 88.53 76.56 72.38 88.89

The final data input format for each student

Serial number Field name
1 Student number(ID)
2 Student number(ID)
3 The first semester
4 First semester credit
5 No credit for the first semester
6 The first of the semester
7 The second semester
8 Second t semester credit
9 No credit for the second semester
10 The second of the semester
11 Second semester average
Langue:
Anglais
Périodicité:
1 fois par an
Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
RSS Feed de la revue