Applying Data-Driven APO Algorithms for Formative Assessment in English Language Teaching

This study proposes an innovative approach for improving the accuracy and efficiency of formative assessment in English language teaching. The method integrates the Artificial Protozoa Optimization (APO) algorithm with the Kernel Extreme Learning Machine (KELM) to overcome limitations such as local optima in traditional models. The study utilizes data from five university-level English courses, consisting of 327 samples divided into a training set (70%), validation set (15%), and test set (15%). The APO-KELM model is constructed by optimizing the KELM parameters using the APO algorithm. Comparative analysis is conducted against other models, including ELM, KELM, WOA-KELM, PPE-KELM, and AOA-KELM, in terms of accuracy (RMSE), MAPE (Mean Absolute Percentage Error), and convergence speed. The result shows that the APO-KELM model demonstrates superior performance with a Root Mean Square Error (RMSE) of 0.6204, compared to KELM (0.7210), WOA-KELM (0.6934), PPE-KELM (0.6762), and AOA-KELM (0.6451). In terms of MAPE, APO-KELM achieves 0.48, outperforming KELM (0.55), WOA-KELM (0.52), PPE-KELM (0.51), and AOA-KELM (0.49). Additionally, the APO-KELM model converged within 300 iterations, showing faster convergence compared to other models. The integration of the APO algorithm with the KELM significantly enhances the accuracy and efficiency of formative assessment in English language teaching. The APO-KELM model is more accurate and faster than traditional models, making it a valuable tool for improving assessment systems. Future research should focus on refining the APO algorithm for broader applications in educational assessments.

@article{Zhou2024,
  title     = {Applying Data-Driven APO Algorithms for Formative Assessment in English Language Teaching},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Guojun Zhou},
  doi       = {10.14569/IJACSA.2024.0151158},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151158}
}