Title: Construction and application of online learning mental state diagnosis model based on student learning behaviour data

Authors: Xiaohui Ma; Zhongwang Li

Addresses: Department of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng, 475000, China; Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China ' Department of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng, 475000, China

Abstract: This study addresses the issue of burnout psychology in online learning, which has become prevalent due to educational reforms and the push for educational informatisation, leading to a disinterest in learning among students. It defines the concept and dimensions of online learning burnout psychology using student data, and develops an early warning model using the XGBoost algorithm to predict student burnout effectively. Results indicate the XGBoost algorithm outperforms three other classification algorithms in iteration quality, with minimal difference between actual and training loss, and demonstrates an average absolute error between 1.5 and 2.0, and a mean square error around 1.0. In tests, the model's accuracy, recall rate, and F1 score were 93.1%, 93.5%, and 0.93, respectively, surpassing comparative models. Thus, this early warning model is highly effective for diagnosing online learning burnout, offering significant improvements over existing methods.

Keywords: learning data; online diagnosis; educational psychology; promotion of information technology; reform in education.

DOI: 10.1504/IJES.2024.143714

International Journal of Embedded Systems, 2024 Vol.17 No.1/2, pp.24 - 35

Received: 07 Nov 2023
Accepted: 07 Jan 2024
Published online: 06 Jan 2025 *

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