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Research on Network Resources Integration of University English Blended Learning Model Based on Multi-Objective Optimization

  
21 mar 2025

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Figure 1.

Comparison of algorithms
Comparison of algorithms

Figure 2.

Achievement interval of control class
Achievement interval of control class

Figure 3.

Achievement interval of experimental class
Achievement interval of experimental class

The results of the model and the algorithm

- Maximum adaptive value Maximum average adaptive value Algorithm running time/ms Finding the probability of a feasible solution
Random extraction algorithm (general model) Indescribable - >1000 81%
Retrospective test method (general model) Indescribable - >1000 97.00%
Standard genetic algorithm (Model of this article) 0.68 0.53 207 100%
Improved genetic algorithm (Model of this article) 0.8 0.71 200 100%

English test results

Test Class Mean Standard deviation T P
Pretest Experimental class 65.11 15.268 0.392 0.721
Control class 66.32 15.174
Posttest Experimental class 67.86 13.739 -3.306 0.016
Control class 77.26 14.956

Test difficulty and distinction

- Pretest Posttest
Difficulty 0.45 0.42
Differentiating 0.36 0.39

Hardware environment

Hardware environment Concrete configuration
Processor Intel core i3 M350 @ 2.27GHz
Memory Samsung DDR3 1067MHz 2G
Hard disk WDC WD3200BEVT-08A23T1 320GB
Operating system Microsoft Windows Server 2010
Compiler Microsoft Visual C++ 6.0

Group results

Alternative solution difficulty Differentiating Usage frequency Completion time Final exposure time Overall quality
1 0.92 0.84 0.73 0.82 0.62 0.82
2 0.77 0.8 0.65 0.93 0.97 0.8
3 0.82 0.84 0.74 0.88 0.89 0.8
4 0.61 0.91 0.63 0.85 0.77 0.82
5 0.42 0.81 0.71 0.81 0.8 0.8