Title: An evaluation of the impact of multimedia online live teaching on students' academic performance

Authors: Hua Liu

Addresses: College of Education, Fuyang Normal University, Fuyang, Anhui, 236037, China

Abstract: To address the limitations of low recall, precision, and evaluation accuracy in conventional evaluation approaches, a new method for evaluating the impact of multimedia online live teaching on students' academic performance is proposed. The random forest algorithm is used to screen the influencing factors, and principal component analysis is adopted to reduce the dimensionality of the influencing factor data. The dimensionality-reduced data is input into the optimised BP neural network for iterative training to obtain relevant evaluation results. Experimental outcomes indicate that the multimedia online live teaching in accordance with the proposed approach can attain a maximum recall rate of 98.15%, a maximum precision rate of 98.74%, and an evaluation accuracy rate varying from 90.2% to 96.8% when it comes to the factors affecting students' academic performance.

Keywords: multimedia; online live teaching; student; academic performance; impact evaluation; random forest algorithm; principal component analysis; PCA: GA optimised BP neural network.

DOI: 10.1504/IJCEELL.2025.150067

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.6, pp.530 - 547

Received: 20 Dec 2024
Accepted: 15 Aug 2025
Published online: 28 Nov 2025 *

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