计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 422-426.

• 大数据与数据挖掘 • 上一篇    下一篇

基于在线学习行为分析的个性化学习推荐

陈晋音, 方航, 林翔, 郑海斌, 杨东勇, 周晓   

  1. 浙江工业大学信息工程学院 杭州 310000
  • 出版日期:2019-02-26 发布日期:2019-02-26
  • 作者简介:陈晋音(1982-),女,副教授,硕士生导师,主要研究方向为智能计算、优化计算、网络安全等,E-mail:chenjinyin@zjut.edu.cn;方 航(1996-),男,硕士生,主要研究方向为聚类分析和深度学习,E-mail:201505910304@zjut.edu.cn;林 翔(1995-),男,硕士生,主要研究方向为进化计算和深度学习,E-mail:201403080215@zjut.edu.cn。

Personal Learning Recommendation Based on Online Learning Behavior Analysis

CHEN Jin-yin, FANG Hang, LIN Xiang, ZHENG Hai-bin, YANG Dong-yong, ZHOU Xiao   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310000,China
  • Online:2019-02-26 Published:2019-02-26

摘要: 随着在线课程和线上学习的普及,大量的在线学习行为数据被积累。如何利用数据挖掘技术分析积累的大数据,从而为教学决策和学习优化提供服务,已经成为新的研究重点。文中分析了在线学习的行为特征,挖掘学习者的性格特征与学习效率的关系,实现个性化学习方法推荐。首先,提取在线学习行为特征,并提出了一种基于BP神经网络的学习成绩预测方法,通过分析在线学习行为特征,预测其相应的线下学习成绩;其次,为了进一步分析学习者的在线学习行为与成绩的关系,提出了基于实际熵的在线学习行为规律性分析,通过分析学习者的在线学习行为,定义并计算相应的实际熵值来评估个体的学习行为规律性,从而分析规律性与最终成绩的关系;再次,基于Felder-Silverman性格分类法获得学习者的性格特征,对学习者实现基于K-means的聚类分析获得相似学习者的类别,将学习成绩较优的学习者的在线学习习惯推荐给同一类别的其他学习者,从而提高学习者的在线学习效率;最终,以某在线课程平台的实际数据为实验对象,分别实现在线学习行为特征提取、线下成绩预测、学习规律性分析和个性化学习推荐,从而验证了所提方法的有效性和应用价值。

关键词: 在线学习行为, BP神经网络, 实际熵, Felder-Silverman性格分析, 个性化学习推荐

Abstract: With the wide use of online courses and the population of online learning,massive data of online learning behaviors have been collected.How to take advantages of those accumulated data through novel data mining technology for improving teaching decision and learning efficiency is becoming the research focus.In this paper,online learning behavior features are extracted,relationship between online learner’s personality and learning efficiency is modeled and analyzed,and personal learning recommendation is designed as well.First,online learner behavior features were extracted,and BP neural network based academic performance prediction algorithm was put forward,in which offline score was predicted based on accordingly online learning behavior features.Second,in order to further analyze the relationship of online learning behavior and offline practical score,a novel actual entropy based online learning behavior orderness evaluation model was proposed.Each learner’s offline academic performance can be predicted on basis of online learning orderness.Third,learners’ personalities were estimated through Felder-Silverman method.K-means algorithm was carried out on those personality vectors to achieve clusters of learners with the similar personality.Among those learners clustered into the same class,the top scored learner’s learning behavior will be recommended to the rest learners.Finally,tackinga practical online courses platform’s data as our experimental subject,plenty of experiments were carried out including online learning behavior feature extraction,offline academic performance evaluation and orderness analysis,perso-nal learning behavior recommendation,and the efficiency and application value of proposed method was proved.

Key words: Online learning behavior, BP neural network, Actual entropy, Felder-Silverman personality analysis, Personal learning recommendation

中图分类号: 

  • TP181
[1]COATES H.Student engagement in campus-based and online education:University connections[OL].http://www.bokus.com/cgi-bin/product_search.cgi?authors=Hamish%20Coates.
[2]STRANG K.How student behavior and reflective learning impact grades in online business courses[J].Journal of Applied Research in Higher Education,2016,8(3):390-410.
[3]PRIOR D D,MAZANOV J,MEACHEAM D,et al.Attitude,digital literacy and self efficacy:Flow-on effects for online lear-ning behavior[J].Internet & Higher Education,2016,29:91-97.
[4]BUTCHER K R,SUMNER T.How Does Prior Knowledge Impact Students’ Online Learning Behaviors?[J].International Journal of Cyber Behavior Psychology & Learning,2011,1(4):1-18.
[5]YANG C,HSIEH T.Regional differences of online learning behavior patterns[J].Electronic Library,2013,31(2):167-187.
[6]PARK Y,YU J H,JO I H.Clustering blended learning courses by online behavior data:A case study in a Korean higher education institute[J].Internet & Higher Education,2016,29:1-11.
[7]SHIMADA A,OKUBO F,YIN C,et al.Informal Learning Behavior Analysis Using Action Logs and Slide Features in E-Textbooks[C]∥International Conference on Advanced Learning Technologies.IEEE,2015:116-117.
[8]HWANG W Y,SHADIEV R,WANG C Y,et al.A pilot study of cooperative programming learning behavior and its relationship with students’ learning performance[J].Computers & Edu-cation,2012,58(4):1267-1281.
[9]TOUYA K,FAKIR M.Mining Students’ Learning Behavior in Moodle System[J].Journal of Information Technology Research (JITR),2014,7(4):12-26.
[10]YE C,KINNEBREW J S,SEGEDY J R,et al.Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences[C]∥Proceedings of the 8th International Conference on Educational Data Mining.2015:380-383.
[11]LINAN L C,ANGEL ALEJANDRO JUAN PEREZ.Educatio-nal data mining and learning analytics:differences,similarities and time evolution[J].Ruse Revista De Universidad Y Sociedad Del Conocimiento,2015,12(3):98-112.
[12]DURKSEN T L,CHU M W,AHMAD Z F,et al.Motivation in a MOOC:a probabilistic analysis of online learners’ basic psychological needs[J].Social Psychology of Education,2016,19(2):241-260.
[13]FITOUSSI J P,VELUPILLAI K.Technology for Mining the Big Data of MOOCs[J].Research & Practice in Assessment,2014,9:29-37.
[14]MAC CALLUM K,JEFFREY L.Factors Impacting Teachers’ Adoption of Mobile Learning[J].Journal of Information Technology Education Research,2014,13(13):141-162.
[15]樊超,宗利永.MOOC在线学习行为的人类动力学分析[J].开放教育研究,2016,22(2):53-58.
[16]宗阳,孙洪涛,张享国,等.MOOCs学习行为与学习效果的逻辑回归分析[J].中国远程教育,2016,36(5):14-22.
[17]肖建忠,陈小娟,贾秀险.高等教育评估多元化研究[J].高教探索,2013(1):13-15.
[18]O’CONNOR M C,PAUNONEN S V.Big Five personality predictors of post-secondary academic performance[J].Personality &Individual Differences,2007,43(5):971-990.
[19]POROPAT A E.A meta-analysis of the five-factor model of personality and academic performance[J].Psychological Bulletin,2009,135(2):322-328.
[20]VEDEL A.The Big Five and tertiary academic performance:A systematic review and metaanal-ysis[J].Personality & Indivi-dual Differences,2014,71(2):66-76.
[21]KONTOYIANNIS I,ALGOET P H,SUHOV Y M,et al.Nonparametric entropy estimation for stationary processes and random fields,with applications to English text[J].IEEE Transactions on Information Theory,1998,44(3):1319-1327.
[22]CAO Y,GAO J,LIAN D,et al.Orderness Predicts Academic Performance:Behavioral Analysis on Campus Lifestyle[J].eprint arXiv:1704.04013.
[23]TOKTAROVA V I,PANTUROVA A A.Learning and Tea-ching Style Models in Pedagogical Design of Electronic Educational Environment of the University[OL].http://www.mc-ser.org/journal/index.php/mjss/article/view/6874.
[24]倍智人才研究院.大五人格心理学:The big five[M].北京:企业管理出版社,2015.
[25]PERRY T W.16-Cattle Finishing Systems[OL].http://doi.org/10.1016/B978-012552052-2150019-6.
[26]王晨煜,管明辉,殷传涛,等.基于FelderS-ilverman学习风格模型的网络学习风格研究[J].重庆理工大学学报,2017,31(2):102-109.
[27]FREUND Y,MASON L.The Alternating Decision Tree Lear-ning Algorithm[C]∥Machine Learning:Sixteenth International Conference.1999:124-133.
[28]MOZINA M,DEMSAR J,KATTAN M,et al.Nomograms for Visualization of Bayesian Classifier[C]∥European Conference on Principles of Data Mining & Knowledge Discovery.2004:337-348.
[1] 宋岩, 胡瑢华, 郭福民, 袁新亮, 熊睿洋. 基于sEMG的改进SVM+BP肌力预测分层算法[J]. 计算机科学, 2020, 47(6A): 75-78.
[2] 周立鹏, 孟利民, 周磊, 蒋维, 董建平. 基于BP神经网络的摔倒检测算法[J]. 计算机科学, 2020, 47(6A): 242-246.
[3] 诸珺文. 基于改进BP神经网络的SQL注入识别[J]. 计算机科学, 2020, 47(6A): 352-359.
[4] 陈燕文,李坤,韩焱,王燕平. 基于MFCC和常数Q变换的乐器音符识别[J]. 计算机科学, 2020, 47(3): 149-155.
[5] 刘晓彤,王伟,李泽禹,沈思婉,姜小明. 基于改进BP神经网络的尿液中红白细胞识别算法[J]. 计算机科学, 2020, 47(2): 102-105.
[6] 马创, 周代棋, 张业. 基于改进鲸鱼算法的BP神经网络水资源需求预测方法[J]. 计算机科学, 2020, 47(11A): 486-490.
[7] 许飞翔,叶霞,李琳琳,曹军博,王馨. 基于SA-BP算法的本体概念语义相似度综合计算[J]. 计算机科学, 2020, 47(1): 199-204.
[8] 郭佳. 基于改进的人工神经网络对存储系统性能进行预测的方法[J]. 计算机科学, 2019, 46(6A): 52-55.
[9] 陈晋音, 王桢, 陈劲聿, 陈治清, 郑海斌. 基于深度学习的智能教学系统的设计与研究[J]. 计算机科学, 2019, 46(6A): 550-554.
[10] 李婷婷, 毕海权, 王宏林, 王晓亮, 周远龙. 基于BP神经网络的地铁站厅空调负荷预测[J]. 计算机科学, 2019, 46(11A): 590-594.
[11] 刘玉成, 理查德·丁, 张颖超. 一种BPNNs识别算法的医学检测泛实时性问题研究[J]. 计算机科学, 2018, 45(6): 301-307.
[12] 潘俊虹, 王宜怀, 吴薇. 基于优化BP神经网络的物理量回归方法[J]. 计算机科学, 2018, 45(12): 170-176.
[13] 杨风开, 程素霞. 基于GA-BP神经网络的双摄像机位姿视觉调节方法[J]. 计算机科学, 2018, 45(11A): 185-188.
[14] 陈维鹏, 敖志刚, 郭杰, 余勤, 童俊. 基于改进的BP神经网络的网络空间态势感知系统安全评估[J]. 计算机科学, 2018, 45(11A): 335-337.
[15] 徐洋, 陈燚, 黄磊, 谢晓尧. 基于多层BP神经网络和无参数微调的人群计数方法[J]. 计算机科学, 2018, 45(10): 235-239.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 编辑部. 新网站开通,欢迎大家订阅![J]. 计算机科学, 2018, 1(1): 1 .
[2] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[3] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[4] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[5] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[6] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[7] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[8] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[9] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[10] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .