融入在线学习自我效能感的多维度学习者模型构建及推荐OA

The learner model is an important foundation for effectively recommending personalized learning resources.Although it has widely integrated multiple dimensions of learner characteristics,it has not incorporated online learning self-efficacy,a key indicator for measuring learners' psychology and learning motivation.Therefore,self-efficacy of online learning is introduced and a comprehensive evaluation method that combines static and dynamic approaches is proposed,using scales and a series of designed behavioral indicators to achieve a comprehensive assessment of online learning self-efficacy.Diversified features such as learning interests,current levels,and learning styles is integrated to construct a multi-dimensional learner model and an improved neural collaborative filtering method that incorporates the learner model is designed.Experimental results show that the multi-dimensional learner model has significantly better performance in recommendation methods than the comparison methods that do not consider this factor,providing a new perspective for the construction of learner models and enabling more effective personalized and precise learning resource recommendations for learners.