Design And Implementation Of Primary School Mathematics Auxiliary Learning System Based On Deep Learning

With the development of society,people are more and more aware of the importance of learning for future development,and the investment in children’s education is gradually increasing.However,the popular photo search question assisted learning software on the market may have a negative effect on children’s learning.Children may directly copy the answers they get from the search instead of trying to learn how to answer them.This system is different from the traditional photo search problem.The traditional photo search problem is to find out the original question and answer directly from the database and display it directly to the user.The search solution is to find out the topic consistent with the search question solving method and display the solution.The user can solve his own problems only by learning the exercises solution,so as to avoid copying the answers and so on.In addition,with the development of urbanization,education shows the imbalance and insufficiency of education.Educational resources are mainly concentrated in developed areas and wealthy families.For poor areas and families,educational resources are relatively scarce and can not meet the educational needs of students.This system can alleviate this phenomenon.In order to solve the above problems,this paper starts with primary school mathematics and uses artificial intelligence technology to develop a new auxiliary learning system.In this paper,the most important functions of the system are realized by using external OCR technology,self-developed text error correction technology and self-developed question solution matching technology.In order to solve the problem of scarcity of text error correction data set,the construction rules of generating pseudo data are designed.In model training,the pseudo data is used to train the base model,and then the final model is trained on the real data based on the base model.Finally,the error correction accuracy is improved by post-processing,so that the accuracy reaches 80.5%.The problem solution matching algorithm is divided into two stages:recall and sorting.Sentence Bert model and Bert model are used respectively.The overall accuracy rate is 84.5% and the recall rate is 74.3%,which is higher than expected.In addition,before the problem solution matching,the text error correction algorithm is applied to the OCR result error correction,so that the matching result can be effectively improved.When students finish their homework after class,they often don’t know whether they have done it right.Therefore,the system provides manual online correction of homework,which makes the correction results more reliable and humanized.At the same time,in order to facilitate students to consolidate and practice the current course after class,the system provides the function of unit synchronous learning.In addition,in order to facilitate users to retrieve data,the system also provides users with data search function and displays popular downloaded data.Through the above functions,an auxiliary learning system is constructed,which not only enables children in remote areas to enjoy the same educational resources as urban children,but also helps students solve some learning problems.In the implementation process of this project,the mainstream framework SSM is used to build the background service,and the vant framework is used to build the wechat applet interface.I mainly participated in the design and implementation of text error correction algorithm,problem solution matching algorithm,personal center module,job management module and unit synchronous learning module,as well as the test of the system.