Method of generating personalized learning path based on multi-course knowledge graph merging and system thereof

A method of generating a personalized learning path based on multi-course knowledge graph merging, wherein the method comprises constructing a personalized knowledge base, wherein a step of constructing a personalized knowledge base is as follows:

• A, collecting physiological data signals of students, comprising electroencephalogram signals, electrocardiogram signals and video signals, through a head-mounted electroencephalogram device, a patch electrocardiogram device and a suspended monitoring device;
• B, extracting key features from the collected physiological data signals to obtain key data features of students, comprising electroencephalogram, electrocardiogram, body temperature, posture and facial expression features;
• C, designing a multimodal physiological data merging mining method to perform data mining and predict learning preferences of students;
• D, updating a basic knowledge graph according to the predicted learning preferences, and generating a personalized knowledge graph of all courses conforming to learning conditions of students;
• E, collecting a personalized knowledge graph of all courses that students need to learn in the current academic year to generate a personalized knowledge base exclusive to each individual;
• wherein obtaining the personalized knowledge graph of all courses that students need to learn in the current academic year further comprises the following steps:
• Step 1:
• using an entity alignment technology to realize the personalized knowledge graph merging of all courses, and automatically generating a personalized knowledge graph of multi-course merging by establishing an index table;
• wherein realizing the personalized knowledge graph merging of all courses comprises the following steps:
• 1.1. extracting knowledge points and structure information of all personalized knowledge graphs of students in the current academic year, wherein the index table comprises knowledge points and relationships;
• wherein a step of designing a structure of the index table is as follows:
• 1.1.1. numbering documents of a plurality of teaching materials;
• 1.1.2. taking key words of knowledge points in the knowledge graph as key values;
• 1.1.3. recording the space occupied by knowledge points in four types:
• basic concept explanation, application explanation, intensive explanation and testing;
• 1.1.4. a document frequency, wherein a frequency of knowledge points appearing in a document collection is recorded;
• 1.1.5. associated document ID;
• 1.1.6. a lexical term frequency, wherein a frequency of key words appearing in a specific document is recorded;
• 1.1.7. position information, wherein the specific position information of key words in the document is recorded;
• 1.1.8. relationship information, which is used to describe the connection and association between knowledge points;
• 1.2. using a knowledge point representation learning method for representation learning according to the index table;
• wherein a step of designing knowledge point representation learning is as follows:
• 1.2.1. learning an initial embedding vector of an entity and a relationship by using a TransR model:
• training the TransR model by minimizing conversion errors among a head entity, a relationship and a tail entity to obtain the initial embedding vectors of each entity and relationship;
• 1.2.2. determining a plurality of different levels of embedding vectors according to task requirements:
• setting two levels of embedding vectors, in which one dimension is used to represent surface semantics, and the other dimension is used to represent deeper semantics;
• 1.2.3. for each entity and relationship, merging the plurality of learned embedding vectors, and connecting the plurality of embedding vectors to form a higher-dimensional embedding representation to obtain the TransR model with multi-level embedding;
• 1.2.4. using the TransR model with multi-level embedding obtained in Step 1.2.3 for joint training:
• in the training process, using relation triplets, entity attribute information of the knowledge graph and context information of the knowledge graph to assist in learning more accurate embedding representation;
• 1.2.5. iterating the training process, constantly optimizing parameters of multi-level embedding and the TransR model with multi-level embedding, and obtaining a final entity embedding vector and a relationship embedding vector;
• wherein the relationship comprises one or more factors of an entity attribute value, relationship information and semantic association;
• 1.3. calculating the similarity between the knowledge point and the relationship according to the index table to perform tasks of knowledge point alignment and link prediction;
• 1.4. automatically generating a personalized knowledge graph of multi-course merging;
• wherein a process step of entity alignment is as follows:
• 1a. calculating the similarity between the obtained entity embedding vector and the relationship embedding vector, representing the position of the entity in the semantic space by the output entity embedding vector, capturing the semantic similarity between entities, and performing the entity alignment task by calculating the similarity between entity vectors;
• 1b. capturing the semantic differences between different relationships by the output relationship embedding vector, and performing the link prediction task by calculating the similarity between the relationship vectors to obtain the personalized knowledge graph of multi-course merging;
• wherein a similarity calculation method comprises cosine similarity, Euclidean distance and Mahalanobis distance;
• Step 2, generating a subsequent learning path according to a current learning progress of users and the personalized knowledge graph of multi-course merging;
• Step 3:
• evaluating the generated learning path in terms of link prediction accuracy;
• wherein the evaluation method of Step 3 is as follows:
• evaluating the generated learning path by using an accuracy index of link prediction, in which the link prediction accuracy is:
• ACC
• =
• P_r
• R_r
• where P_r denotes the link prediction result;
• and R_r denotes the real link result, ACC∈[0,1].