Leveraging Neural Network for Online Learning Performance Prediction and Learning Suggestion

Shen, Yingshan; Liu, Weiwei; Wu, Qiong; Chen, Ruiyang; Liu, Kui

doi:10.1007/978-3-030-38778-5_29

Cited by 1 publication

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“…Based on the above understanding, this paper will make the evaluation of English course learning quality more scienti c and quantitative through intelligent technology. e prior guide samples are obtained by the entropy method, then the neural set network model is optimized to learn the prior sample knowledge by using the adaptive mutation genetic algorithm, and an evaluation model is established, which reduces the subjectivity of the neural set network learning samples [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

Song

2022

Computational Intelligence and Neuroscience

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The rationality and timeliness of the comprehensive results of English course learning quality are increasingly important in the process of modern education. There are some problems in the scientific evaluation of English course learning quality and teachers’ own English course learning, such as the need for proper adjustment and improvement. Based on the improved network theory of genetic algorithm, this paper takes an online English course learning quality evaluation model and uses MATLAB 7.0 to write the graphical user interface of the neural set network English course learning quality prediction model. The model uses the genetic algorithm of adaptive mutation to optimize the initial weights and values of the neural set network and solves the problems of prediction accuracy and convergence speed of English course learning quality evaluation results. Simulation experiments show that the neural set network has a strong dependence on the initial weights and thresholds. Using the improved genetic algorithm to optimize the initial weights and thresholds of the neural set network reduced the time for the neural set network to find the weights and thresholds that meet the training termination conditions, the prediction accuracy was increased to 0.897, the prediction accuracy was 78.85%, and the level prediction accuracy was 84.62%, which effectively promoted the development of online English course learning in colleges and the continuous improvement of teachers’ English course learning level.

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Section: Introductionmentioning

confidence: 99%

Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

Song

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Leveraging Neural Network for Online Learning Performance Prediction and Learning Suggestion

Cited by 1 publication

References 6 publications

Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

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