Title: Construction of online English teaching quality evaluation model based on PCA-SSA-SVM algorithm

Authors: Jingjia Guo; Lei Zhang; Naiyuan Bao

Addresses: School of Foreign Languages, Dalian Jiaotong University, Dalian, 116000, China ' School of Foreign Languages, Dalian Jiaotong University, Dalian, 116000, China ' Academic Affairs Office, Northeast Normal University, Jilin, 130000, China

Abstract: This study proposes a hybrid algorithm for objective online English teaching quality evaluation. Principal component analysis (PCA) reduces feature dimensions and extracts key components, sparrow search algorithm (SSA) optimises support vector machine (SVM) parameters, and SVM performs classification/prediction. Experiments on the PISA dataset demonstrate the model's superiority: optimal convergence in five iterations, 1% average classification error, 98.7% fit degree, and 45.2 s training time, outperforming other methods. The algorithm's stability, accuracy, and efficiency enable rapid model updates, aiding timely teaching strategy adjustments. Results confirm its enhanced problem-solving, pattern recognition, and generalisability, offering a robust solution for standardised, data-driven quality assessment in online education.

Keywords: support vector machine; SVM; sparrow search algorithm; SSA; principal component analysis; PCA; online English language teaching; quality assessment.

DOI: 10.1504/IJWET.2026.153361

International Journal of Web Engineering and Technology, 2026 Vol.21 No.1, pp.46 - 73

Received: 11 Jul 2024
Accepted: 25 Mar 2025
Published online: 06 May 2026 *

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