KG-based memory recommendation algorithm for learning path  

作　　者：Wang Danzhi Xiang Jianxin Cui Yansong 

机构地区：[1]School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2023年第2期36-48,共13页中国邮电高校学报（英文版）

摘　　要：In intelligent education,most student-oriented learning path recommendation algorithms are based on either collaborative filtering methods or a 0-1 scoring cognitive diagnosis model.Unfortunately,they fail to provide a detailed report about the students’mastery of knowledge and skill and explain the recommendation results.In addition,they are unable to offer realistic learning path recommendations based on students’learning progress.Knowledge graph based memory recommendation algorithm(KGM-RA)was proposed to solve these problems.On the one hand,KGM-RA can provide more accurate diagnosis information by continuously fitting the students’knowledge and skill proficiency vector(SKSV)in a multi-level scoring cognitive diagnosis model.On the other hand,it also proposes the forgetting recall degree(FRD)according to the statistical results of the human forgetting phenomenon.It also calculates closeness centrality in the knowledge graph to achieve the recommended recall effect consistent with the human forgetting phenomenon.Experiments show that the KGM-RA can obtain the actual learning path recommendations for students,provides the adjustable ability of FRD,and has better reliability and interpretability.

关 键 词：learning path recommendation knowledge graph cognitive diagnosis human forgetting phenomenon 

分 类 号：G434[文化科学—教育学] TP391.3[文化科学—教育技术学]