Application of Three-Layer Stacked LSTM Model Assisted by Educational Robots in Children’s Learning

Abstract

To enable educational robots to better accompany children’s learning and growth, first, the encoder part of the image description model is established based on the convolutional neural network (CNN), and the decoder part of the image description model is built by the long short-term memory (LSTM). The attention mechanism is combined with the input stage, LSTM, and the output stage of the model. Second, three-layer stacked LSTM is used to improve and optimize the model. Artificial intelligence (AI) challenger data set is used to test the constructed model, and bilingual evaluation understudy (BLEU) and metric for evaluation of translation with explicit ordering (METEOR) are used to evaluate the model. Finally, 48 children are used as the research objects to study the application effect of the model. The results show that the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 values of the constructed model are higher than those of the neural image caption (NIC) model during the entire iteration process. This model begins to converge when the number of iterations is 20,000. However, the NIC model starts to converge when the number of iterations is 45,000. The constructed model has a faster convergence speed. The scores of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 of the constructed model are 0.687, 0.54, 0.432, and 0.348, respectively, and the score of METEOR is 0.187, which is higher than other models. In the actual application, the learning efficiency and effect of children who use this model for learning are better than the traditional teaching effect. The research results can help to improve the auxiliary role of educational robots in children’s learning, which has very important practical significance.