Title: A prediction method for teaching effect of college English online course based on learner behaviour mining

Authors: Meng Su

Addresses: Department of Public Basic Teaching, Luoyang Polytechnic, Luoyang, 471000, China

Abstract: Aiming at the problems of low mining accuracy, poor prediction accuracy and long time in traditional prediction methods, this paper proposes a prediction method of college English online course teaching effect based on learner behaviour mining. The k-means algorithm is used to cluster the learner behaviour data, and the fuzzy association rules are combined to realise the learner behaviour mining. The teaching effect prediction index system of college English online course is built, and the teaching effect prediction of college English online course is realised according to the calculation results of index weight and PSO-BP neural network. The results show that the maximum accuracy of the proposed method is 96.9%, the range of prediction accuracy is 95.7%~97.9%, and the maximum prediction time is 1.63 s.

Keywords: learner behaviour mining; college English; online course; teaching effect prediction; fuzzy association rules; PSO-BP neural network.

DOI: 10.1504/IJCEELL.2025.146014

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.3/4, pp.232 - 252

Received: 18 Jun 2024
Accepted: 06 Nov 2024
Published online: 01 May 2025 *

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