Article: An evaluation of online English teaching learning effectiveness based on decision tree classification algorithm Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.7 pp.117 - 135 Abstract: To enhance the accuracy and real-time performance in evaluating online English teaching effectiveness, this study proposes an evaluation model based on the decision tree classification algorithm. First, an evaluation index system is established, and principal component analysis (PCA) is employed to screen the most relevant indicators. The filtered data is then pre-processed for analysis. Given that online English teaching data is typically a hybrid data type, the classification and regression trees (CART) algorithm is utilised for the evaluation task. Furthermore, to mitigate model overfitting, an improved algorithm that incorporates decision tree depth as a pruning weight is introduced. Experimental results demonstrate that the proposed method achieves a maximum R<SUP align="right"><SMALL>2</SMALL></SUP> value of 0.974, a maximum inconsistency index of only 0.29, and a maximum evaluation time of 5.80 seconds. These findings indicate that the method possesses high evaluation accuracy and promising real-time performance. Inderscience Publishers - linking academia, business and industry through research

Title: An evaluation of online English teaching learning effectiveness based on decision tree classification algorithm

Authors: Yingying Qi

Addresses: Xinxiang Vocational and Technical College, Xinxiang, 453000, Chinaz

Abstract: To enhance the accuracy and real-time performance in evaluating online English teaching effectiveness, this study proposes an evaluation model based on the decision tree classification algorithm. First, an evaluation index system is established, and principal component analysis (PCA) is employed to screen the most relevant indicators. The filtered data is then pre-processed for analysis. Given that online English teaching data is typically a hybrid data type, the classification and regression trees (CART) algorithm is utilised for the evaluation task. Furthermore, to mitigate model overfitting, an improved algorithm that incorporates decision tree depth as a pruning weight is introduced. Experimental results demonstrate that the proposed method achieves a maximum R² value of 0.974, a maximum inconsistency index of only 0.29, and a maximum evaluation time of 5.80 seconds. These findings indicate that the method possesses high evaluation accuracy and promising real-time performance.

Keywords: online English teaching; assessment of learning outcomes; decision tree classification algorithm; tree depth.

DOI: 10.1504/IJCEELL.2026.151826

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.7, pp.117 - 135

Received: 29 May 2025
Accepted: 07 Oct 2025
Published online: 20 Feb 2026 *

Title: An evaluation of online English teaching learning effectiveness based on decision tree classification algorithm

Keep up-to-date