Research And Implementation Of Assisted Learning Algorithm For Youth Programming Education

Youth programming education is an important content of youth education.Because programming education is different from traditional education,it is necessary to study special auxiliary learning methods for youth programming education,so as to improve the effectiveness of youth programming education.Encouraged by the policies of various countries,many domestic and foreign programming education languages and platforms have emerged.However,no matter whether it is domestic or foreign,there is no more personalized education model for young people's programming education,but a traditional popular education model.Therefore,the main research problem of this article is the convenience of obtaining information through the Internet for youth programming education,and an auxiliary learning algorithm for youth programming education is proposed.This article completes the following work by reading relevant literature at home and abroad and experimental research:1.This article uses the most authoritative CHC theory(Cattell-Horn-Carroll fluidcrystal theory)in cognitive ability as the theoretical basis for the definition of youth's programming behavior,and with the help of the Internet,it successfully defines and provides the programming behavior of youth The method of obtaining data;using the programming module features of the Scratch programming platform to define programming topics,and also provide a method of obtaining relevant data.2.This paper presents an improved feature selection algorithm based on random forest and recursive feature elimination(RF-RFE for short),and uses it as a regression prediction algorithm for the auxiliary learning model of adolescent programming education.Regression models were created for young people's programming behavior and scores of programming problems and difficulty of programming problems,respectively,and the most representative characteristic attributes of young people's programming behaviors were selected.3.Through the construction of knowledge graphs on young people's programming behavior and programming topics,the establishment of auxiliary learning models for young people's programming education is completed.The main innovations of this article are:1.Aiming at the personalized requirements of young people's programming education,this paper proposes the concept of an auxiliary learning model that combines the Internet,young people's programming education tools,and young people's programming education based on knowledge graph.2.Combine the CHC theory in cognitive ability and the Internet platform to define and collect data on the dimensions of programming behavior and the dimensions of programming topics.3.Aiming at the disadvantage that the traditional RF-RFE algorithm can only choose a single way of iteration,a RGSS-RF-RFE algorithm that adds a randomly generated sequence selection algorithm(RGSS for short)is proposed.Experiments by collecting data related to young people's programming behavior and programming topics have verified that the RGSS-RF-RFE algorithm proposed in this paper has obvious advantages in the selection of feature attributes and the establishment of regression models in the auxiliary learning algorithm of youth programming education.The special storage method of the knowledge graph and the cosine similarity theorem complete the auxiliary learning algorithm for the programming education of the youth.The new method for programming education of the youth proposed in this paper has high theoretical value and application value.