Open AccessPersonalized knowledge point recommendation system based on course knowledge graph
Author(s) -
Yakun Lang,
Guozhong Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012073
Subject(s) - interpretability , computer science , knowledge graph , graph , class (philosophy) , point (geometry) , knowledge extraction , artificial intelligence , theoretical computer science , mathematics , geometry
Existing class education lacks analysis of students’ learning data, which can not reasonably evaluate students’ mastery of knowledge points and locate students’ learning path in real time, nor effectively recommend the required knowledge points for students. In order to solve the above problems, a personalized knowledge point recommendation system model (KG-PKP) combined with the knowledge graph of the course is proposed. By using accuracy, answer-time and answer-types in answer records, evaluation equations which to judge students’ mastery of knowledge points can be constructed. We can extract the knowledge points in answer records and map them into the course knowledge graph. Personalized knowledge points for students can be recommended by using the semantic hierarchical relationship and sequence of knowledge points in the knowledge graph. The comparative experiment proves the validity and interpretability of the model.