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Optimization Strategies of Bayesian Modeling Algorithms for Multilingual Teaching Systems in Southeast Asian Universities

  
24 mar 2025
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Figure 1.

The overall framework of the knowledge tracking model
The overall framework of the knowledge tracking model

Figure 2.

Prediction accuracy of both models
Prediction accuracy of both models

Figure 3.

AUC curve of both models
AUC curve of both models

Figure 4.

RMSE value of both models
RMSE value of both models

Figure 5.

Loss value of both models
Loss value of both models

Figure 6.

Student satisfaction results
Student satisfaction results

The aftertest results of the knowledge level of the two class students

After test number Class Mean SD SE Sig.(Double tail)
p1 Class 1 74.64 7.95 1.44 0.493
Class 2 75.77 7.08 1.24
p2 Class 1 76.88 7.41 1.03 0.133
Class 2 76.15 7.62 1.19
p3 Class 1 78.14 6.52 0.94 0.042
Class 2 77.74 7.59 1.05
p4 Class 1 82.45 5.03 0.82 0.025
Class 2 78.77 6.93 0.95
p5 Class 1 83.59 4.18 0.84 0.006
Class 2 78.18 6.27 0.93
p6 Class 1 85.86 3.82 0.75 0.002
Class 2 79.45 4.59 0.89

Accuracy of the knowledge status tracking results

Weeks Valid number Evaluation dimension
L(0-70%) M(70%-85%) H(85%-100%)
1 48 14.8% 27.7% 57.5%
2 47 11.8% 22.7% 65.5%
3 48 8.3% 15.9% 75.8%
4 48 6.8% 12.4% 80.8%
5 47 5.2% 9.7% 85.1%
6 48 3.9% 5.8% 90.3%
Average 8.5% 15.7% 75.8%
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro