Intelligent Evaluation and Optimization of Postgraduate Education Comprehensive Ability Training under the Mode of “One Case, Three Systems”

This study aims to explore the intelligent evaluation and optimization methods for the comprehensive ability training of graduate students under the mode of "one case, three systems" to improve the quality and effect of graduate training. Firstly, a weighted clustering algorithm for mixed attributes is designed. Secondly, an evaluation model of postgraduate training quality based on sampling method and ensemble learning is established. Finally, the algorithm's and the model's performance are compared and tested. The test results show that with the increase in the number of experiments, the accuracy of the proposed weighted clustering algorithm can reach more than 90%, which is improved by 10%. The average number of iterations is 276, and the accuracy and F1 value can achieve the highest level with fewer iterations and stable algorithm performance. Compared with the R1 model, F1 and the accuracy of the model proposed in this study are enhanced by 3.29% and 6.75%, respectively. The feature-weighted clustering algorithm and the training quality evaluation model designed here complement each other and jointly construct a more elaborate and comprehensive training system. The feature-weighted clustering algorithm oriented to mixed attributes for the first time combines sampling methods and ensemble learning in the education ability training. Moreover, a multi-dimensional and intelligent postgraduate training evaluation framework is constructed, which provides a new idea for improving the quality of postgraduate training.

@article{Xiang2023,
  title     = {Intelligent Evaluation and Optimization of Postgraduate Education Comprehensive Ability Training under the Mode of “One Case, Three Systems”},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yong Xiang and Zeyou Chen and Liyu Lu and Yao Wei},
  doi       = {10.14569/IJACSA.2023.0141272},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141272}
}