Personalized test question recommendation method based on unified probalilistic matrix factorization

doi:10.11772/j.issn.1001-9081.2017082071

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (3): 639-643.DOI: 10.11772/j.issn.1001-9081.2017082071

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Personalized test question recommendation method based on unified probalilistic matrix factorization

LI Quan, LIU Xinghong, XU Xinhua, LIN Song

Education College of Information Technology, Hubei Normal University, Huangshi Hubei 435002, China

Received:2017-08-23 Revised:2017-10-20 Online:2018-03-07 Published:2018-03-10
Supported by:
This work is partially supported by the Education Science "Twelfth Five-Year" Planning Project of Hubei Province (2011-B130), the Planning Project of Science and Technology of State Archives Bureau (2016-x-51), the Planning Project of Outstanding Young and Middle-aged Scientific and Technological Innovation Team of Universities and Colleges of Hubei Province (T201515), the Science and Technology Project of Education Department of Hubei Province (D20142504).

基于联合概率矩阵分解的个性化试题推荐方法

李全, 刘兴红, 许新华, 林松

湖北师范大学教育信息与技术学院, 湖北黄石 435002

通讯作者: 李全
作者简介:李全(1982-),男,湖北黄陂人,讲师,硕士,主要研究方向:机器学习、数据挖掘;刘兴红(1969-),女,湖北蕲春人,教授,硕士,主要研究方向:大数据、教育信息化;许新华(1968-),男,湖北孝感人,教授,硕士,主要研究方向:数据库、数据挖掘;林松(1978-),男,湖北黄石人,讲师,硕士,主要研究方向:搜索引擎、数据挖掘。
基金资助:
湖北省教育科学"十二五"规划项目（2011B130）；国家档案局科技计划项目（2016-x-51）；湖北省高等学校优秀中青年科技创新团队计划项目（T201515）；湖北省教育厅科技项目（D20142504）。

Abstract

Abstract: 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 factorization; 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.

Key words: online education, test question resource, recommendation system, unified probalilistic matrix factorization, cognitive diagnosis

摘要： 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。

关键词: 在线教育, 试题资源, 推荐系统, 联合概率矩阵分解, 认知诊断

CLC Number:

TP301

LI Quan, LIU Xinghong, XU Xinhua, LIN Song. Personalized test question recommendation method based on unified probalilistic matrix factorization[J]. Journal of Computer Applications, 2018, 38(3): 639-643.

李全, 刘兴红, 许新华, 林松. 基于联合概率矩阵分解的个性化试题推荐方法[J]. 计算机应用, 2018, 38(3): 639-643.

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Personalized test question recommendation method based on unified probalilistic matrix factorization

基于联合概率矩阵分解的个性化试题推荐方法

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