Title: An evaluation method of ChatGPT intervention in online course teaching effectiveness based on principal component regression analysis

Authors: Hui Wang; Haibin Wang; Shaomei Li

Addresses: Academic Affairs Department, North University of China, Taiyuan, 030000, Shanxi, China ' Academic Affairs Department, North University of China, Taiyuan, 030000, Shanxi, China ' School of Education, Shaanxi Normal University, Xi'an, 710000, Shaanxi, China

Abstract: In order to overcome the limitations of low recall rate and low accuracy of evaluation indicators in traditional online course teaching effectiveness evaluation methods, a new evaluation method of ChatGPT intervention in online course teaching effectiveness using principal component regression analysis is proposed. Principal component regression analysis is adopted to screen evaluation indicators to establish an evaluation index system for ChatGPT intervention in online course teaching effectiveness. The evaluation index data is clustered using Gaussian mixture model, and then input into RBF neural network to obtain the evaluation results of ChatGPT intervention in online course teaching effectiveness. The experimental results show that the proposed method achieves a maximum recall rate of 98.74% in evaluation indicates, a minimum screening time of 1.28 seconds, and evaluation accuracy ranging from 63.8% to 85.8%.

Keywords: principal component regression analysis; ChatGPT intervention; online course; teaching effectiveness evaluation; Gaussian mixture model; GMM; RBF neural network.

DOI: 10.1504/IJCEELL.2026.152141

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.1/2, pp.173 - 188

Received: 03 Dec 2024
Accepted: 30 Sep 2025
Published online: 09 Mar 2026 *

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