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

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 question-answering 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.