Title: A method for predicting learning achievements in online education based on behavioural characteristics

Authors: Yan Zhang

Addresses: School of Accounting, Tongling University, Tongling, 244061, China

Abstract: Aiming at the problems of low prediction accuracy, large error in extracting behavioural feature data, and long prediction time in traditional online education learning achievement prediction methods, a behavioural feature based online education learning achievement prediction method is proposed. Firstly, the three-dimensional S-F-T model is used to divide and analyse the students' online education and learning behaviour. Then, according to the analysis results, the random forest algorithm is used to calculate the weight of behaviour characteristics data, and select the characteristics of students' online education and learning behaviour. Finally, a prediction function is constructed through the local self-attention mechanism, and the selected network education learning behaviour feature data is input as samples into the constructed prediction function, outputting the final prediction result. The test results show that the proposed method has good prediction accuracy, low error in extracting behavioural features, and short prediction time.

Keywords: behavioural characteristics; online education learning; performance prediction; S-F-T model; random forest; local self-attention mechanism.

DOI: 10.1504/IJRIS.2025.147450

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.3, pp.200 - 208

Received: 06 Apr 2023
Accepted: 23 May 2023
Published online: 16 Jul 2025 *

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