All Element Selection Method in Classroom Social Networks and Analysis of Structural Characteristics

To deeply investigate the complex relationship between learners' structural characteristics in classroom social networks and the dynamics of learning emotions in smart teaching environments, an innovatively improved RP-GA. All Element Selection Method based on genetic algorithm is proposed. The method calculates the importance of factors based on the random forest model and guides the population initialization together with random numbers to achieve the differentiation and efficiency of factor selection; and utilized the Partial Least Squares regression model in conjunction with a cross-validation optimization model to enhance the accuracy of fitness evaluation, efficiently tackling the issues of premature convergence and low prediction accuracy inherent in traditional genetic algorithms for factor selection. Based on this method, the elements affecting learning emotions are precisely screened, and the intrinsic links between elemental changes and structural properties are deeply analyzed. Experiments show that RP-GA selects a small and efficient number of key elements on public datasets and significantly improves the prediction performance of classifiers such as SVM, NB, MLP, and RF. The proposed learning sentiment all-essential selection method provides effective conditions for classroom network structure characterization and future learning sentiment computation.

@article{Shou2025,
  title     = {All Element Selection Method in Classroom Social Networks and Analysis of Structural Characteristics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Zhaoyu Shou and Zhe Zhang and Jingquan Chen and Hua Yuan and Jianwen Mo},
  doi       = {10.14569/IJACSA.2025.0160251},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160251}
}