Learning trajectory analysis in English learning using LSTM neural networks

This article uses long short-term memory (LSTM) to capture the dynamic changes of learners during the learning process, improve the modeling ability of learners' learning trajectories, and generate personalized learning suggestions and feedback. This article collects learning data from 8 learners from March to June 2020, including characteristics such as learning duration, learning frequency, and learning performance. LSTM model is adopted to model and predict these time series data. To validate the effectiveness of the model, the model is evaluated using evaluation indicators such as mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) and is compared with Transformer, Gated Recurrent Unit (GRU), recurrent neural network (RNN), and Hidden Markov Model (HMM). The experimental results show that the MAE, RMSE, and R2 of LSTM's prediction of English learning performance are 0.08, 0.06, and 0.98, respectively, and the MAE, RMSE, and R2 of LSTM's prediction of English learning duration are 0.09, 0.10, and 0.97, respectively. The prediction error of LSTM is lower than that of Transformer, GRU, RNN, and HMM, and this maintains high stability in the prediction of 8 learners. Visual analysis of learning trajectories shows that some learners exhibit intermittent learning states with significant fluctuations in learning performance, while others tend to stabilize after a significant increase in performance at specific stages, indicating that their learning strategies are effective in the early stages but then enter a learning bottleneck period. Some learners exhibit a decline in performance, suggesting that their current learning strategies are ineffective. This article highlights the advantages of the LSTM model in predicting English learning outcomes. By dynamically analyzing learners' progress and trajectories, the model enables the development of personalized and targeted learning recommendations, helping learners refine and optimize their strategies for improved performance.