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 ...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.展开更多
文摘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.
