Research And Implementation Of Personalized Learning Path Recomendation System Based On Knowledge Map

With the continuous acceleration of digitalization in China,online education has also been fully and widely applied.Compared to traditional classroom teaching,online teaching has lifted strict time and location restrictions,allowing learners to access learning resources in any subject and field anytime and anywhere.In order to facilitate learners to effectively explore and organize massive online learning resources and plan learning plans reasonably,learning path recommendation has gradually become one of the important research topics in online education,which has improved learning effectiveness to a certain extent.At present,most relevant studies judge the degree of adaptation between learners and learning resources based on their cognitive level,learning style,learning tendency,and other characteristics,and rank and recommend learning resources based on corresponding rules.However,they do not make use of learners’ constantly changing learning behavior,the correlation between knowledge points,etc.The generated learning paths may violate the internal logic between knowledge points Disadvantages such as inability to match learners’ constantly changing knowledge levels.To address the above issues,this article conducts research on a personalized learning path recommendation system based on a knowledge graph in junior high school mathematics.This article designs a personalized learning path recommendation method based on knowledge graph.This method first maps the set of knowledge points and target knowledge points that students have already mastered into a knowledge graph,obtaining a personalized knowledge point directed graph that includes the starting and ending points;Next,the Page Rank algorithm is used to measure the centrality of personalized knowledge points in a directed graph;Then,based on the collection of learners’ constantly changing interactive learning sequences,the CKT model is used to measure students’ personalized prior knowledge and personalized chemistry learning rate,predicting the probability of students acquiring personalized knowledge points in the directed graph without mastering knowledge points;Finally,based on the Dijkstra algorithm,personalized learning paths are recommended to learners by combining centrality and predicted values.The experimental results indicate that this method can personalized recommend learning paths that are suitable for learners’ own knowledge state and learning rate,thereby helping learners to reasonably and efficiently complete the learning of target knowledge points.On the basis of implementing a personalized learning path recommendation method,this article completes the design and implementation of a personalized learning path system.This system adopts the Spring Boot framework and Vue framework as a whole,visualizes the knowledge graph using the Neo4 j graph database,and stores the basic data tables using the My Sql database.The system mainly includes functions such as online learning,learning path recommendation,and knowledge point retrieval.It can provide the most matching learning path based on the learners’ knowledge status and learning speed from person to person,thereby helping learners quickly achieve learning goals.