Design of an online education student learning status evaluation model based on dual-improved neural networks

•Designed a student learning status assessment model based on the multi-task cascaded convolutional neural network (MTCNN) for evaluating students' learning status in online education.•Utilized facial image acquisition function to extract facial features of students and classified them through label classification.•Analyzed students' attention and learning emotions using the model.•Demonstrated high accuracy and potential application in online education based on experimental results. With the continuous development of network technology, online education has become an important form of education. However, in the online education model, it is difficult for educators to effectively evaluate students' learning status, and using a learning status evaluation model can effectively solve this problem. The main goal of this model is to comprehensively evaluate students' learning behavior, progress, and outcomes, in order to understand their learning status, provide effective teaching feedback to teachers, help students improve learning methods, and improve learning efficiency. The current automatic evaluation model for student learning status has certain limitations in terms of applicability and accuracy. A student learning state evaluation model based on Multi task Cascaded Convolutional Networks (MTCNN) is proposed to address the effectiveness of online education student learning state evaluation. Use the facial image acquisition function to extract students' facial features, process each feature through label classification, and then analyze the students' attention and learning emotions. Finally, analyze the effectiveness of the research method application. The results showed that the train_loss value of the learning state evaluation model proposed in the study can be reduced to about 0.1; the train_acc value can reach more than 95 %, and the overall volatility is small; the overall evaluation accuracy of facial expressions can reach 74.71 %, which is significantly better than cpc, VGG19 and other evaluation methods; compared with the comprehensive evaluation results and multi-modal analysis methods, only two evaluations at the critical value are different. The experimental results show that the online education students’ learning status evaluation model designed by the research has a high accuracy rate and has a certain application potential in the field of online education.