Data Mining Research Of Online Education Based On Deep Learning Knowledge Tracking Model

Using online education for data mining is a hot topic in the field.The fundamental purpose of this problem is to use machine learning tools to process data in online education and to find valuable information.It has always been a research problem of education discipline to carry out cognitive research with traditional ideas,but with the rise of online education,it has become necessary to use computers to process large amounts of data.One powerful tool for machine learning to process large amounts of data is deep learning,a technique based on neural networks that can be used to simulate nonlinear structures because of its large number of parameters.Combining with the memory network obtained after processing the original data to store the students' state and topic information,a deep learning model combining the deep learning network and memory matrix can well simulate the learning process of students and conduct supervised learning training with the accuracy of predicted responses.Using deep learning for real-time modeling or guided recommendation is a highly practical technique.In previous studies,some model structures of deep learning have been proposed.Firstly,the deep knowledge tracking model is based on cyclic neural network and long and short time memory network.The subsequent research model DKVMN extended the memory function of the model and improved the prediction performance.The latest research models include the query structure model KQN and the deep learning model GKT based on the graph structure for knowledge tracking.Reinforcement learning techniques are often used to model the prediction process as a partially observable markov process,and deep learning is used for model training.In this paper,a deep knowledge tracing model method based on improved graph structure is proposed,which can better find the causal relationship between knowledge and track the students' knowledge state by using the network structure of knowledge points.The experiment on the same data set with the existing algorithm shows that the AUC is used to significantly improve the prediction probability of students.On the basis of the proposed model,the recommendation algorithm is further studied,and DQN is used to build the recommendation model.The recommended reinforcement learning model was trained by using the knowledge tracking model as a simulation student.Get a system that can automatically recommend questions according to the response status at a certain time,and use this system can quickly improve the overall level of all knowledge.After the training test on the open data set,it can be observed that the speed of knowledge acquisition increases gradually in the training process.