基于知识追踪机的多特征融合习题推荐模型

A multi-feature fusion exercise recommendation model based on knowledge tracing machines

个性化习题推荐是智慧教育个性化服务领域的重要课题,然而传统的习题推荐算法对于学生特征的研究不够彻底,对于学生知识掌握与答题行为之间的关联信息挖掘未能充分,导致推荐精准度不佳。为解决上述问题,结合知识追踪机(KTM)和基于用户的协同过滤算法,提出一种基于KTM多特征融合的习题推荐模型SKT-MFER。该模型首先构造了一个融入学生学习行为和学习能力的知识追踪模型KTM-LC,精准挖掘学生的知识掌握水平;接着设置两次筛选,先利用知识点掌握矩阵初步筛选出相似的学生,再根据认知状态相似度和习题难度相似度组合而成的综合相似度进行二次筛选,双重过滤以保障习题推荐的准确度。通过广泛的实验证明,所提方法相比于一些现有的基线模型有更好的效果。

The subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education.Nevertheless,traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship between knowledge mastery and questionanswering behaviors,leading to low recommendation accuracy.To address these issues,combining the knowledge tracing machine and the user-based collaborative filtering algorithm,as a KTM-based multi-feature fusion exercise recommendation model,SKT-MFER was proposed.Firstly,as a knowledge tracking model,KTM-LC,incorporating student learning behaviors and learning abilities,was constructed to accurately assess the student’s knowledge mastery level.Subsequently,two filters were implemented to ensure the exercise recommendation’s accuracy:the first was an initial screening utilizing the knowledge point mastery matrix to eliminate students who were similar to the target student,and the second was a filtering process considering the combined similarity of cognitive state similarity and exercise difficulty similarity.Through extensive experiments,it proves that the proposed method yields better results than some existing baseline models.