基于联合概率矩阵分解的个性化试题推荐方法

Personalized test question recommendation method based on unified probalilistic matrix factorization

近年来随着在线教育中试题资源数量爆炸式的增长,学生很难在海量的试题资源中找到合适的试题,因此面向学生的试题推荐方法应运而生;然而,传统的基于概率矩阵分解的试题推荐方法没有考虑学生的知识点掌握信息,导致推荐结果准确率低,为此,提出一种基于联合概率矩阵分解的个性化试题推荐方法。首先,通过认知诊断模型得到的学生知识点掌握信息;然后,结合学生、试题和知识点三者信息进行联合概率矩阵分解;最后,根据难度范围进行试题推荐。实验结果表明,与其他传统推荐方法相比,所提方法在不同难度试题推荐的准确率上取得了较好的推荐结果。

In recent years, test question resources in online education has grown at an explosive rate. It is difficult for students to find appropriate questions from the mass of question resources. Many test question recommendation methods for students have been proposed to solve this problem. However, many problems exist in traditional test question recommendation methods based on unified probalilistic matrix faetorization; especially information of student knowledge points is not considered, resulting in low accuracy of recommendation results. Therefore, a kind of personalized test question recommendation method based on unified probalilistic matrix factorization was proposed. Firstly, through a cognitive diagnosis model, the student knowledge point mastery information was obtained. Secondly, the process of unified probalilistic matrix factorization was executed by combining the information of students, test questions and knowledge points. Finally, according to the difficulty range, the test questions were recommended. The experimental results show that the proposed method gets the best recommedation results in the aspect of accuracy of question recommendation for different range of difficulty, compared to other traditional recommendation methods, and has a good application prospect.