基于知识点的个性化习题推荐研究
发布时间：2018-10-29 19:07
【摘要】：推荐系统作为解决信息过载问题的重要工具,逐渐渗透并改变着人们的生活方式。个性化习题推荐作为推荐系统在教育领域的分支,近年来,吸引了不少研究学者展开了广泛的研究。本文针对个性化习题推荐算法准确率不高的问题,提出一种基于知识点的个性化习题推荐方案,主要工作和创新点如下:(1)针对认知诊断模型不能对知识点掌握概率化的问题,提出学生知识点掌握概率模型,进而提出TopN个性化习题推荐算法。实验表明,该算法在准确率方面具有较好的效果,在FrcSub数据集比认知诊断模型提高了 10.1%,在Math1数据集比认知诊断模型提高了 2.84%,在Math2数据集不仅比认知诊断模型提高了 1.39%,而且运行效率是认知诊断模型的12.1倍。(2)提出一种表征知识点层次关系的权重图,构建知识点之间的层次关系和学生知识点失分率矩阵,解决了学生知识点之间相互孤立造成的数据稀疏性引起的推荐准确率不高的问题。实验表明,该算法在自建数据集上具有较好的效果。较基于知识点无层次图的个性化习题推荐算法在准确率提高了 12.35%,召回率提高了 5.49%,F1 提高了 11.91%。(3)为了解决现有的评价指标不能准确反映学生对知识点掌握情况的问题,提出一种基于知识点的评价指标,以便更准确地为学生推荐掌握薄弱的知识点习题。为了验证提出的习题推荐方法的合理性,本文设计并实现了一个基于知识点个性化习题推荐系统原型。试用结果表明,该系统可以作为一种重要的辅助教学手段,不仅弥补了不同程度的学生的知识漏洞,而且提高了学生自主学习效率。
[Abstract]:As an important tool to solve the problem of information overload, recommendation system is gradually infiltrating and changing people's way of life. As a branch of recommendation system in the field of education, personalized exercise recommendation has attracted a lot of researchers to carry out extensive research in recent years. Aiming at the problem that the accuracy of personalized exercise recommendation algorithm is not high, this paper proposes a personalized exercise recommendation scheme based on knowledge point. The main work and innovations are as follows: (1) aiming at the problem that the cognitive diagnosis model can not grasp the probability of knowledge point, the paper puts forward the probability model of students' knowledge point mastery, and then puts forward the TopN personalized exercise recommendation algorithm. The experimental results show that the algorithm has a good effect on accuracy, which is 10.1% higher in FrcSub data set than in cognitive diagnosis model, 2.84% higher in Math1 data set than cognitive diagnosis model. The Math2 dataset is not only 1.39 times more efficient than the cognitive diagnostic model, but also is 12.1 times more efficient than the cognitive diagnostic model. (2) A weight map representing the hierarchical relationship of knowledge points is proposed. Constructing the hierarchical relationship between knowledge points and the rate matrix of students' knowledge points can solve the problem of low recommendation accuracy caused by the data sparsity caused by the isolation of students' knowledge points. Experiments show that the algorithm has good effect on self-built data set. Compared with the personalized exercise recommendation algorithm based on the knowledge point without hierarchy graph, the accuracy of the algorithm is increased 12.35%, and the recall rate increases 5.49%. F1 raised 11.911.In order to solve the problem that the existing evaluation indexes could not accurately reflect the students' mastery of knowledge points, a kind of evaluation index based on knowledge point was put forward. In order to more accurately recommend students to grasp the weak points of knowledge exercises. In order to verify the rationality of the proposed exercise recommendation method, this paper designs and implements a prototype of personalized exercise recommendation system based on knowledge point. The experimental results show that the system can be used as an important auxiliary teaching method, which not only makes up for the gaps of students' knowledge, but also improves the efficiency of students' autonomous learning.
【学位授予单位】：西北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3
本文编号：2298536
[Abstract]:As an important tool to solve the problem of information overload, recommendation system is gradually infiltrating and changing people's way of life. As a branch of recommendation system in the field of education, personalized exercise recommendation has attracted a lot of researchers to carry out extensive research in recent years. Aiming at the problem that the accuracy of personalized exercise recommendation algorithm is not high, this paper proposes a personalized exercise recommendation scheme based on knowledge point. The main work and innovations are as follows: (1) aiming at the problem that the cognitive diagnosis model can not grasp the probability of knowledge point, the paper puts forward the probability model of students' knowledge point mastery, and then puts forward the TopN personalized exercise recommendation algorithm. The experimental results show that the algorithm has a good effect on accuracy, which is 10.1% higher in FrcSub data set than in cognitive diagnosis model, 2.84% higher in Math1 data set than cognitive diagnosis model. The Math2 dataset is not only 1.39 times more efficient than the cognitive diagnostic model, but also is 12.1 times more efficient than the cognitive diagnostic model. (2) A weight map representing the hierarchical relationship of knowledge points is proposed. Constructing the hierarchical relationship between knowledge points and the rate matrix of students' knowledge points can solve the problem of low recommendation accuracy caused by the data sparsity caused by the isolation of students' knowledge points. Experiments show that the algorithm has good effect on self-built data set. Compared with the personalized exercise recommendation algorithm based on the knowledge point without hierarchy graph, the accuracy of the algorithm is increased 12.35%, and the recall rate increases 5.49%. F1 raised 11.911.In order to solve the problem that the existing evaluation indexes could not accurately reflect the students' mastery of knowledge points, a kind of evaluation index based on knowledge point was put forward. In order to more accurately recommend students to grasp the weak points of knowledge exercises. In order to verify the rationality of the proposed exercise recommendation method, this paper designs and implements a prototype of personalized exercise recommendation system based on knowledge point. The experimental results show that the system can be used as an important auxiliary teaching method, which not only makes up for the gaps of students' knowledge, but also improves the efficiency of students' autonomous learning.
【学位授予单位】：西北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3
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