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A study on the efficiency and accuracy of neural network model to optimize personalized recommendation of teaching content

Weihang Zhang

Weihang Zhang

Hebei Key Laboratory of Children’s Cognition and Digital Education, Langfang Normal UniversityLangfang, China
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Zhang, Weihang
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Xinjiang Mi

Xinjiang Mi

Hebei Key Laboratory of Children’s Cognition and Digital Education, Langfang Normal UniversityLangfang, China
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Mi, Xinjiang
  
21 mar 2025
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Data publikacji: 21 mar 2025
Otrzymano: 08 lis 2024
Przyjęty: 16 lut 2025
DOI: https://doi.org/10.2478/amns-2025-0547
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© 2025 Weihang Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Framework of cognitive diagnosis model
Framework of cognitive diagnosis model

Figure 2.

Schematic diagram of CUPMF recommendation algorithm
Schematic diagram of CUPMF recommendation algorithm

Figure 3.

Convolutional network diagram of CUPMF model
Convolutional network diagram of CUPMF model

Figure 4.

Joint probability matrix decomposition frame of CUPMF model
Joint probability matrix decomposition frame of CUPMF model

Figure 5.

Student cognitive portrait
Student cognitive portrait

Figure 6.

Student learning status portrait
Student learning status portrait

The statistics of datasets

Statistical term ASSISTMents2015 EdNet
Number of students 4286 1200
Number of exercises 18026 13624
Knowledge points 131 195
Number of answer records 289653 1125341
Number of correctly answered exercises 201328 836725
Number of incorrect answers to exercises 88325 228616

Experimental data

Network node 5 knowledge points in each chapter
Data 15623 student history answer data
KU relationship
Recommended number of test questions n
Number of question banks

Overall results on student performance prediction

Model ASSISTMents2015 EdNet
ACC↑ RMSE↓ AUC↑ ACC↑ RMSE↓ AUC↑
IRT 0.6555 0.5361 0.6092 0.6921 0.4654 0.7118
DINA 0.6659 0.5337 0.6788 0.6894 0.4871 0.6826
MIRT 0.7033 0.4695 0.7251 0.7047 0.4476 0.7273
NeuralCD 0.7406 0.4481 0.7605 0.7166 0.4304 0.7784
RCD 0.7421 0.4349 0.7938 0.7299 0.4239 0.7865
This article 0.7826 0.4271 0.8072 0.7497 0.4043 0.7907

Comparison of indicator data for test results of class C student

Algorithm/model Precision Recall F1 MAE CRR¯ std. deviation KU
Random 0.6062 0.7651 0.6764 0.0285 0.0051 1,2,4,5
DT 0.7036 0.8247 0.7594 0.0275 0.0118 2,3,4
IRT 0.6796 0.8247 0.7452 0.0223 0.0138 1,2,4,3
PMF 0.7637 0.8839 0.8194 0.0182 0.0032 1,2,4
CUPMF 0.8046 0.8921 0.8461 0.0141 0.0029 1,2,4

Comparison of indicator data for test results of class A student

Algorithm/model Precision Recall F1 MAE CRR¯ std. deviation KU
Random 0.5562 0.7544 0.6403 0.0525 0.0235 1,2,4
DT 0.6164 0.7613 0.6812 0.0387 0.0016 2,3,4
IRT 0.6599 0.5836 0.6194 0.0656 0.0421 1,2,4,5
PMF 0.8599 0.8774 0.8686 0.0308 0.0045 1,2,4
CUPMF 0.8953 0.9105 0.9028 0.0147 0.0018 1,2,4

Comparison of indicator data for test results of class B student

Algorithm/model Precision Recall F1 MAE CRR¯ std. deviation KU
Random 0.6648 0.7534 0.7063 0.0289 0.0182 1,2,4,3
DT 0.7081 0.7521 0.7294 0.0226 0.0129 1,2,4
IRT 0.7541 0.8453 0.7971 0.0188 0.0141 1,2,4
PMF 0.8028 0.9052 0.8509 0.0111 0.0023 1,2,4
CUPMF 0.8517 0.9121 0.8809 0.0136 0.0019 1,2,4
Język:
Angielski
Częstotliwość wydawania:
1 razy w roku
Dziedziny czasopisma:
Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne
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