A personalized knowledge recommendation method integrating multi-knowledge point and group characteristics
Personalized knowledge recommendation is a crucial issue in smart education,aiming to provide personalized learning services and improve learning outcomes.Existing recommendation methods based on deep knowledge tracing have difficulty directly handling comprehensive exercises that involve multiple knowledge points.Relying solely on the learner's own knowledge state for recommendations poses challenges such as data sparsity and single-result outcomes.This paper pro-poses a personalized knowledge point recommendation method that integrates multi-points fusion deep knowledge tracking and group characteristics collaborative filtering(MDKT-GCCF).The proposed MDKT-GCCF method treats multiple knowl-edge points corresponding to exercises as exercise features and introduces multi-hot encoding to represent the relationship between exercises and knowledge points,thus modeling the learner's knowledge state.Based on the knowledge level of tar-get learner and the neighbor information of the learner group,similar learners are identified,and a collaborative filtering al-gorithm is used to obtain group learning preferences for knowledge point recommendation.The proposed MDKT-GCCF meth-od better adapts to recommending exercises with multiple knowledge points while also uncovering group learning characteris-tics,enhancing recommendation effectiveness.
万方数据
维普
