Construction and optimization of personalized learning paths for English learners based on SSA-LSTM model

Abstract

With the rapid development of big data and artificial intelligence technology, personalized learning has attracted significant attention in education. This study focuses on constructing and refining personalized learning paths for English learners by integrating the sparrow search algorithm (SSA) with the long short-term memory (LSTM) model. SSA, an intelligent optimization algorithm, exhibits robust global search capabilities and swift convergence, while the LSTM model excels in processing time series data. This study employs the LSTM model to analyze English learners' behavior data, subsequently optimizing the LSTM model's hyperparameters using SSA to enhance prediction accuracy and generalization. Results demonstrate that the personalized learning path generated by the SSA-LSTM model outperforms the traditional LSTM model and other comparative models across multiple evaluation metrics, offering a more precise prediction of learners' needs and providing educators with a scientific and efficient personalized teaching tool.