基于深度自编码器和二次协同过滤的个性化试题推荐方法

摘要/Abstract

摘要： 个性化试题推荐是实现高效学习的有效途径,帮助学生从“题海战术”中解脱出来,对实现适应性教学、促进教育公平具有重要意义。但目前个性化试题推荐方法大多是基于协同过滤进行试题层面的个性化推荐,没有聚焦到知识点层面,存在推荐试题定位不准确的问题。针对上述问题,对基于深度自编码器和二次协同过滤的个性化试题推荐方法进行了研究。首先考虑到学生对知识点的认知情况进行基于知识点的二次协同过滤试题推荐,然后应用项目反应理论和深度自编码器来预测学生在推荐试题上涉及推荐知识点的得分以及综合得分,最后对预测结果协同判断并控制最终个性化推荐试题的难度,产生最终的推荐试题列表。通过对比实验验证提出的推荐方法的推荐结果相对于传统试题推荐更具个性化和准确性。

关键词: 个性化学习, 深度学习, 试题推荐, 协同过滤, 自编码器

Abstract: Personalized question recommendation is an effective way to improve learning efficiency.It helps students get rid of the “Massive Questions” and has important significance to achieve adaptive teaching and promote education equity.However,most of the personalized question recommendation methods are based on collaborative filtering without focusing on the knowledge points,which causes the problem that the positioning of the recommended questions are inaccurate.In order to solve this problem,a personalized question recommendation system based on deep autoencoder and a two-step collaborative filtering was adopted in this paper.Firstly,considering students’ master degree of knowledge points,the two-step collaborative filtering question recommendation based on knowledge points is realized.Secondly,item response theory and deep autoencoder are used to predict the scores and the comprehensive scores of the students involving recommended knowledge points on the recommended questions.Finally,the prediction results are synergistically decided,the difficulty of the final personalized recommendation questions is controlled,and a list of final recommended questions in generated.Comparison experiments verify that the recommended results of the proposed recommendation method are more personalized and accurate than that of traditional question recommendation methods.

Key words: Auto encoder, Collaborative filtering, Deep learning, Personalized learning, Personalized question recommendation

中图分类号:

TP301

熊慧君, 宋一凡, 张鹏, 刘立波. 基于深度自编码器和二次协同过滤的个性化试题推荐方法[J]. 计算机科学, 2019, 46(11A): 172-177. https://doi.org/

XIONG Hui-jun, SONG Yi-fan, ZHANG Peng, LIU Li-bo. Personalized Question Recommendation Based on Autoencoder and Two-step Collaborative Filtering[J]. Computer Science, 2019, 46(11A): 172-177. https://doi.org/

参考文献

[1]杨现民,唐斯斯,李冀红.发展教育大数据:内涵、价值和挑战[J].现代远程教育研究,2016(1):50-61.
[2]杜婧敏,方海光,李维杨,等.教育大数据研究综述[J].中国教育信息化,2016(19):1-3.
[3]OXMAN S,WONG W,INNOVATIONS D V X.WhitePaper:Adaptive Learning Systems[EB/OL].[2015-01-02].http://www.integratededsolution.com/wp-content/uploads/2014/10/DVx-Adaptive-Learning-White-Paper-February-20131.pdf.
[4]徐立芳,莫宏伟,李金,等.智能教育与教育智能化技术研究[J].教育现代化,2018,4(3):115-117,119.
[5]刘邦奇.智慧教育:新时代的教育变革与转型[N].中国教育报,2018-01-27(3).
[6]LORD F.Some test theory for tailored testing[C]∥Computer assisted instruction,testing,and guidance.New York:Harper＆Row,1970:139-183.
[7]BATMAZ Z,YUREKLI A,BILGE A,et al.A review on deep learning for recommender systems:challenges and remedies[J].Artificial Intelligence Review,2018:1-37.
[8]蒋一君,邱飞岳,刘迎春,等.基于错题库的个性化练习生成模型研究[J].中国教育信息化,2011(8):73-74.
[9]郭辰.高中英语诊断性练习系统的需求分析、模块设计和知识体系构建[D].合肥:中国科学技术大学,2013:22.
[10]王文泉.错题管理系统中个性化推荐练习算法的设计与实现[J].中国教育信息化,2016(11):67-70.
[11]申瑞民,汤轶阳,韩鹏.基于概念图的教学内容智能调整模型及算法实现[J].上海交通大学学报,2002(5):102-105.
[12]牛文娟.基于协同过滤的学习资源个性化推荐研究[D].北京:北京理工大学,2014.
[13]杨超.基于粒子群优化算法的学习资源推荐方法[J].计算机应用,2014,34(5):1350-1353.
[14]ADACHE I,FOURNIER S,CHIFU A G.Harnessing Ratings and Aspect-Sentiment to Estimate Contradiction Intensity in Temporal-Related Reviews[J].Procedia Computer Science,2017,112(C):1711-1720.
[15]DASCALU M I,BODEA C N,MOLDOVEANU A,et al.A recommender agent based on learning styles for better virtual collaborative learning experiences[J].Computers in Human Beha-vior,2015,45(45):243-253.
[16]黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):1619-1647.
[17]孙琳琳.认知诊断评估对实现有效诊断教学的促进作用[J].世界最新医学信息文摘,2018,18(20):243.
[18]RAMI S,BENNANI S,IDRISSI M K.Towards a method for analyzing learning style using item response theory[C]∥International Conference on Information Technology Based Higher Education and Training.IEEE,2017:1-5.
[19]SIGKDD:KDD Cup 2010:Student performance evaluation[OL].http://www.kdd.org/kdd-cup/view/kdd-cup-2010-student-performance-evaluation/Data.
[20]KIRK J.jfkirk/tensorrec[OL].https://github.com/jfkirk/tensorrec.

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