基于协同过滤和认知诊断的试题推荐方法

doi:10.11896/jsjkx.180901827

摘要/Abstract

摘要： 智能教育中,试题推荐方法是数据挖掘在教育测量领域的新运用,是自适应测试的智能化和个性化程度的重要体现,目前主流的试题推荐方法有两类,分别是协同过滤试题推荐方法和认知诊断试题推荐方法,前者忽略了独立个体的知识属性,后者缺乏对种群的共性评估。针对上述问题,为提高试题推荐的精确度和效率,综合考虑独立被试者的知识属性和类环境群体的知识共性,文中提出了基于协同过滤和认知诊断的试题推荐方法。首先,设计了基于多级属性评分的认知诊断模型,并利用该模型对被试者的答题情况进行建模;然后,将被试者的知识属性掌握模式用于概率矩阵分解,预测被试者的潜在答题情况;最后,根据信息量指标向被试者动态地推荐合适的试题。试题推荐方法综合考虑了个体的个性特征和群体的共性特征,提高了解释性和可靠性。实验结果表明,相比单协同过滤试题推荐算法和认知诊断选题策略,所提方法的测试效率分别提升了20.35%和2.5%。

关键词: 认知诊断, 认知诊断模型, 试题推荐, 数据挖掘, 协同过滤, 信息量

Abstract: The question recommendation method isthe new application of data mining on the Education Measurement,which is an important performance of the intelligence and personalization in the intelligent education,particularly.At present,there are two types of mainstream test recommendation methods,including the question recommendation based on collaborative filtering and the question recommendation based on cognitive diagnosis.However,the former ignores the knowledge attribute of independent individuals,the latter is lack of the common evaluation.In order to improve the accuracy and efficiency of the question recommendation,comprehensive considering the knowledge attributes of the independent testing subjectand the knowledge commonality of the environment-like groups,this paper proposed a testing recommendation method based on collaborative filtering and cognitive diagnosis.Firstly,the proposed method designs a cognitive diagnosis model based on multi-level attributes scoring,which is used to model the subject’s answer.Then,the subject’s knowledge attribute is used for probabilistic matrix factorization to predict the potential answers.Finally,the appropriate questions are recommended to the subjects according to the information value.The testing recommendation comprehensively improves the interpretability and reliability that the experiment shows the method improves the efficacy by 20.35% and 2.5% respectively compared with collaborative filtering and cognitive diagnosis.

Key words: Cognitive diagnosis, Cognitive diagnosis model, Collaborative filtering, Data mining, Information value, Questions recommendation

中图分类号:

TP301

齐斌, 邹红霞, 王宇, 李冀兴. 基于协同过滤和认知诊断的试题推荐方法[J]. 计算机科学, 2019, 46(11): 235-240. https://doi.org/10.11896/jsjkx.180901827

QI Bin, ZOU Hong-xia, WANG Yu, LI Ji-xing. Questions Recommendation Based on Collaborative Filtering and Cognitive Diagnosis[J]. Computer Science, 2019, 46(11): 235-240. https://doi.org/10.11896/jsjkx.180901827

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