Design and Application of the DPC-K-Means Clustering Algorithm for Evaluation of English Teaching Proficiency

Effective and precise methodologies for evaluating the proficiency in English language instruction are instrumental in enhancing educators' competencies and the effectiveness of educational administrative processes. The objective of this paper is to refine the neutrality and precision of such assessments by introducing a novel approach that leverages an advanced K-means algorithm in conjunction with convolutional neural networks (CNNs). Initially, a thorough examination of the issue at hand leads to the formulation of an assessment framework that integrates both a clustering algorithm and a CNN, with a comprehensive elucidation of the pivotal technical aspects. Subsequently, the paper introduces a data clustering and categorization technique grounded in the DPC-K-means methodology, specifically tailored for indices that measure English teaching proficiency, and employs CNNs to devise a model for evaluating these competencies. The integration of these two components—data clustering and the assessment model—gives rise to an innovative technique. Ultimately, the proposed method is implemented and its practicality is substantiated through an analysis of empirical data from educators' teaching proficiency indices. A comparative analysis with existing algorithms reveals that the proposed method achieves superior clustering performance and the lowest margin of error in predictive assessments.

@article{Niu2024,
  title     = {Design and Application of the DPC-K-Means Clustering Algorithm for Evaluation of English Teaching Proficiency},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mei Niu},
  doi       = {10.14569/IJACSA.2024.0150851},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150851}
}