發(fā)布時間：2018-10-29 19:07

【摘要】：推薦系統(tǒng)作為解決信息過載問題的重要工具,逐漸滲透并改變著人們的生活方式。個性化習題推薦作為推薦系統(tǒng)在教育領(lǐng)域的分支,近年來,吸引了不少研究學者展開了廣泛的研究。本文針對個性化習題推薦算法準確率不高的問題,提出一種基于知識點的個性化習題推薦方案,主要工作和創(chuàng)新點如下:(1)針對認知診斷模型不能對知識點掌握概率化的問題,提出學生知識點掌握概率模型,進而提出TopN個性化習題推薦算法。實驗表明,該算法在準確率方面具有較好的效果,在FrcSub數(shù)據(jù)集比認知診斷模型提高了 10.1%,在Math1數(shù)據(jù)集比認知診斷模型提高了 2.84%,在Math2數(shù)據(jù)集不僅比認知診斷模型提高了 1.39%,而且運行效率是認知診斷模型的12.1倍。(2)提出一種表征知識點層次關(guān)系的權(quán)重圖,構(gòu)建知識點之間的層次關(guān)系和學生知識點失分率矩陣,解決了學生知識點之間相互孤立造成的數(shù)據(jù)稀疏性引起的推薦準確率不高的問題。實驗表明,該算法在自建數(shù)據(jù)集上具有較好的效果。較基于知識點無層次圖的個性化習題推薦算法在準確率提高了 12.35%,召回率提高了 5.49%,F1 提高了 11.91%。(3)為了解決現(xiàn)有的評價指標不能準確反映學生對知識點掌握情況的問題,提出一種基于知識點的評價指標,以便更準確地為學生推薦掌握薄弱的知識點習題。為了驗證提出的習題推薦方法的合理性,本文設計并實現(xiàn)了一個基于知識點個性化習題推薦系統(tǒng)原型。試用結(jié)果表明,該系統(tǒng)可以作為一種重要的輔助教學手段,不僅彌補了不同程度的學生的知識漏洞,而且提高了學生自主學習效率。
[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.
【學位授予單位】：西北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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