Title: Evaluation method of English game teaching classroom-learning effect based on ResNet algorithm

Authors: Liangjie Li

Addresses: Department of Foreign Affairs, Henan Finance University, Zhengzhou, 450000, China

Abstract: Due to the limitations of current methodologies in accurately reflecting the nuances of learning, leading to suboptimal assessment accuracy and efficiency, this study introduces a novel evaluation approach for the learning outcomes in English game-based classrooms, utilising the ResNet algorithm. This method comprehensively considers the academic performance variations of students pre- and post-instruction, establishing an evaluation index system. The selection of indices is informed by grey correlation analysis and multicollinearity analysis. Following this, relevant classroom data are gathered and normalised. Principal component analysis is employed to distil the salient features from the data. Once these numerical and visual data are inputted, the ResNet algorithm is trained to assess the learning impact of English game-based instruction. The findings indicate that the F1-score consistently remains above 0.95, the AUC value approaches 1, and the maximum evaluation duration is 16.837 seconds.

Keywords: English game teaching; learning effect evaluation; ResNet; data processing.

DOI: 10.1504/IJCEELL.2025.146007

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.3/4, pp.331 - 350

Received: 28 Jun 2024
Accepted: 19 Nov 2024
Published online: 01 May 2025 *

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