| Junior high school students generally have different degrees of learning disabilities in physics,but due to the lack of professional researchers in China,the number of students at compulsory education is huge,many people with learning disabilities do not receive timely and effective help.At compulsory education,when students are new to physics,individualized diagnosis and intervention for students with physical learning disabilities is necessary.Experts and scholars in different fields such as pedagogy,psychology and medicine have made in-depth research and practical exploration on learning disabilities,and have made many achievements in definition,diagnosis and intervention.However,the diagnosis of learning disabilities needs the intervention of professionals,in order to carry out a comprehensive and personalized diagnosis of junior high school students,it needs a lot of manpower and material resources,limited to the current ratio of teachers is impossible to achieve.With the development of new technology,complex network theory has been used to study various fields,including education.Many scholars use complex network to study teaching materials and analyze test questions.Therefore,we hope to use the method of complex network for reference to build a scientific diagnosis model and provide students with a self-diagnosis method of physical learning disability that conforms to the cognitive law and is easy to operate.In this paper,the Knowledge Network of Junior Middle School Physics Textbook is established by using the complex network method,and the node characteristics and the whole characteristics of the network are analyzed,but the links between the chapters are weak.This paper designs the diagnosis model of physics learning disability in junior high school from two angles.Firstly,based on the knowledge network established above,combined with the final exam questions in the first volume of Grade Eight,a junior high school physics learning disability diagnosis network is constructed,and the network is used to simulate the diagnosis of learning disability.This is a staged diagnosis of learning disabilities for students who have already learned the physics knowledge in the first volume of the eighth grade.On the premise that students have learned the contents of this semester,pushing test questions from exercises with a large degree of knowledge points will be more suitable for students at the final stage.Then,based on the characteristics of the constructed physics knowledge network in the first volume of the eighth grade of junior high school,the knowledge points in the first chapter are extracted,and then combined with the test questions bank of the existing learning platform,the learning disability diagnosis network for junior high school physics beginners is constructed by complex network method,and the obtained network is analyzed.If there is a learning disability in one of the core knowledge points,it will have a great impact on the subsequent learning.On this basis,the diagnosis model is constructed,and the practical research of learning disability diagnosis for beginners is carried out by using this model.This is a feasible diagnosis process for students who have just come into contact with junior high school physics when they begin to learn the first chapter of the eighth grade.On the premise of students’ understanding of basic concepts,pushing exercises,starting from the exercises of a single knowledge point with a small degree,is more suitable for beginners to diagnose knowledge points that may cause learning disabilities in physics learning.A comparative study of the diagnosis process between this model and the existing personalized learning platform shows that the learning disability diagnosis model constructed by complex network method can be used to diagnose the physics learning disability of junior high school students at different levels,and can accurately locate the knowledge points with learning disability,which provides a way to realize the artificial intelligent learning disability diagnosis method,and puts forward some suggestions for the existing personalized learning platform. |