Study on English learning behavior analysis and teaching optimization based on SSA-GRU

Abstract

With rapid development of information technology, intelligent education has become an important research direction. English learning is an integral part of education, and its learning behavior analysis and teaching optimization have attracted much attention. Based on the SSA-GRU (Singular Spectrum Analysis-Gated Recurrent Unit) model, this study aims to deeply explore characteristics of English learning behavior to provide a scientific basis for teaching optimization. Firstly, a comprehensive learning behavior database is constructed by collecting online learning data of English learners, including learning time, interaction frequency, and homework completion. Subsequently, the SSA-GRU model is used to mine the data deeply, and the potential laws and trends in learning behavior are effectively extracted. The experimental results show that compared with traditional analysis methods, the SSA-GRU model improves the accuracy of behavioral pattern recognition by 15.3% and trend prediction by 12.7%. Further analysis shows that learners’ learning time is positively correlated with their achievements (the correlation coefficient is 0.78), and the influence of interaction frequency on the learning effect is particularly significant (the improvement effect is 18.4%). This study proposes targeted teaching optimization strategies based on these analysis results, including personalized learning path recommendations and interactive teaching activity design. Practical application shows that these strategies have effectively improved the teaching effect, with students’ achievement increasing by 10.2% on average and learning satisfaction rising by 22.5%. This study not only enriches the theory of intelligent education but also provides strong support for English teaching practice, showing the broad application prospect of the SSA-GRU model in English learning behavior analysis and teaching optimization.