深度学习技术在教育大数据挖掘领域的应用分析
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摘要
随着全球人工智能与教育大数据峰会的召开,多国学者探讨了教育变革的新趋势,印证了技术与教育深度融合会带来更多的机遇和挑战。其中,深度学习作为AI领域的热点问题,将成为教育发展的关键。文章通过对相关研究进行筛选统计研究,辨析不同领域深度学习的概念并简要分析典型的深度学习模型及其应用领域;以教育大数据挖掘的特点为基础,总结基于深度学习的教育大数据挖掘目的和流程;系统探讨深度学习在教育大数据挖掘领域的四个应用研究方向和主要应用机构;最后,明确了教育大数据挖掘领域引入深度学习的重要意义,同时,针对教育大数据挖掘所服务的对象和需要解决的问题,提出了深度学习技术在教育大数据挖掘领域进一步发展的意见。
With the convening of the global artificial intelligence and education big data summit,scholars from different countries have discussed the new trend of educational reform, confirming that the deep integration of technology and education will bring more opportunities and challenges. Among them,deep learning, as a hot issue in the field of AI, will become the key to the development of education. After screening related studies, this paper distinguishes the concepts of deep learning in different fields and analyzes the typical deep learning models and their application fields. Based on the characteristics of educational big data mining, this paper summarizes the purpose and process of educational big data mining based on deep learning. Then, four research directions and main application institutions of deep learning in educational big data mining are systematically discussed. Finally, the significance of deep learning in the field of educational big data mining is clarified. Meanwhile, in view of the objects served by educational big data mining and the problems to be solved, suggestions for further development of deep learning in educational big data mining are proposed.
With the convening of the global artificial intelligence and education big data summit,scholars from different countries have discussed the new trend of educational reform, confirming that the deep integration of technology and education will bring more opportunities and challenges. Among them,deep learning, as a hot issue in the field of AI, will become the key to the development of education. After screening related studies, this paper distinguishes the concepts of deep learning in different fields and analyzes the typical deep learning models and their application fields. Based on the characteristics of educational big data mining, this paper summarizes the purpose and process of educational big data mining based on deep learning. Then, four research directions and main application institutions of deep learning in educational big data mining are systematically discussed. Finally, the significance of deep learning in the field of educational big data mining is clarified. Meanwhile, in view of the objects served by educational big data mining and the problems to be solved, suggestions for further development of deep learning in educational big data mining are proposed.
引文
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[29]XING W,DU D.Dropout Prediction in MOOCs:using deep learning for personalized intervention[J].Journal of educational computing research,2018,3:55-62.
[30]WANG W,YU H,MIAO C.Deep model for dropout prediction in MOOCs[C]//Proceedings of the 2nd International Conference on Crowd Science and Engineering.New York:ACM,2017:26-32.
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[34]寇媛媛.基于深度学习的在线教学推荐系统设计与研究[J].西安职业技术学院学报,2017(3):11-14.
[35]HASSAN H A M.Personalized research paper recommendation using deep learning[C]//Proceedings of the 25th Conference on User Modeling,Adaptation and Personalization.New York:ACM,2017:327-330.
[36]刘瑞梅,孟祥增.基于深度学习的多媒体画面情感分析[J].电化教育研究,2018,39(1):68-74.
[37]陈嘉华.基于深度学习的英语语音识别与发音质量评价[D].广州:广东外语外贸大学,2015.
[38]陈珊珊.自动作文评分模型及方法研究[D].哈尔滨:哈尔滨工业大学,2017.
[39]陈一乐.基于回归分析的中文作文自动评分技术研究[D].哈尔滨:哈尔滨工业大学,2016.
[40]刘明杨.高考作文自动评分关键技术研究[D].哈尔滨:哈尔滨工业大学,2015.
[41]李磊.基于机器学习的中小学数学自动阅卷系统研究与实现[D].桂林:广西师范大学,2017.
[42]刘逸雪,卢雨轩,丁亮,等.基于Bi-LSTM的数学主观题自动阅卷方法[J].管理观察,2018(2):109-113.
[43]汪张龙.人工智能技术在考试中的应用[J].中国考试,2017(11):30-36.
[44]YU X,YU H,TIAN X Y,et al.Recognition of college students from Weibo with deep neural networks[J].International journal of machine learning and cybernetics,2017,8(5):1447-1455.
[45]廖鹏,刘宸铭,苏航,等.基于深度学习的学生课堂异常行为检测与分析系统[J].电子世界,2018(8):97-98.
[46]曾志.基于人脸特征与深度学习的学生人格特质分析[D].南昌:江西师范大学,2017.
[47]黄蓓静.深度学习技术在中文人物关系抽取中的应用研究[D].上海:华东师范大学,2017.
[48]李依尘.面向自动问答的中学历史知识库构建[D].哈尔滨:哈尔滨工业大学,2017.
[2]BAKER R S,INVENTADO P S.Educational data mining and learning analytics[M]//Learning analytics.New York:Springer,2014:61-75.
[3]舒忠梅,徐晓东.学习分析视域下的大学生满意度教育数据挖掘及分析[J].电化教育研究,2014(5):39-44.
[4]BECKER S A,CUMMINS M,DAVIS A,et al.NMC horizon report:2017 library edition[R].Austin:The New Media Consortium,2017.
[5]OKOLI C,SCHABRAM K.A guide to conducting a systematic literature review of information systems research[J].Ssrn electronic journal,2011,10:1-49.
[6]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural computation,2006,18(7):1527-1554.
[7]布卢姆.教育目标分类学[M].蒋小平,张琴美,罗晶晶,译.上海:华东师范大学出版社,1986.
[8]MARTON F,S魧LJ魻R.On qualitative differences in learning:I-Outcome and process[J].British journal of educational psychology,1976,46(1):4-11.
[9]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[10]KOUTNIK J,GREFF K,GOMEZ F,et al.A Clockwork RNN[J].Computer science,2014:1863-1871.
[11]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural computation,1997,9(8):1735-1780.
[12]POLLACK J B.The induction of dynamical recognizers[M]//Connectionist approaches to language learning.Boston:Springer,1991:123-148.
[13]KRIZHEVSKY A,SUTSKEVER I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of International Conference on Neural Information Processing Systems.New York:Curran Associates Inc.2012:1097-1105.
[14]周凯龙.基于深度学习的图像识别应用研究[D].北京:北京工业大学,2016.
[15]BADRI H,YAHIA H,DAOUDI K.Fast and accurate texture recognition with multilayer convolution and multifractal analysis[C]//Proceedings of European Conference on Computer Vision.Cham:Springer,2014:505-519.
[16]周燕.基于语音稀疏表示的谎言检测研究[D].苏州:苏州大学,2017.
[17]王冰倩.基于深度学习的语音质量评价方法研究[D].大连:大连理工大学,2015.
[18]李翔.噪声环境下基于深度学习的语音识别研究[D].武汉:武汉工程大学,2017.
[19]吴嘉伟.电子病历实体关系抽取研究[D].哈尔滨:哈尔滨工业大学,2014.
[20]梁军,柴玉梅,原慧斌,等.基于深度学习的微博情感分析[J].中文信息学报,2014,28(5):155-161.
[21]曹宇慧.基于深度学习的文本情感分析研究[D].哈尔滨:哈尔滨工业大学,2016.
[22]TANG S,PETERSON J C,PARDOS Z A.Deep neural networks and how they apply to sequential education data[C]//Proceedings of the Third(2016)ACM Conference on Learning@Scale.New York:ACM,2016:321-324.
[23]KIM B H,VIZITEI E,GANAPATHI V.GritNet:student performance prediction with deep learning[EB/OL].(2018-04-19)[2018-06-15].https://arxiv.org/pdf/1804.07405.pdf
[24]OKUBO F,YAMASHITA T,SHIMADA A,et al.A neural network approach for students'performance prediction[C]//Proceedings of the Seventh International Learning Analytics&Knowledge Conference.New York:ACM,2017:598-599.
[25]WANG L,SY A,LIU L,et al.Deep knowledge tracing on programming exercises[C]//Proceedings of the Fourth(2017)ACM Conference on Learning@Scale.New York:ACM,2017:201-204.
[26]SMITH A,MIN W,MOTT B W,et al.Diagrammatic student models:modeling student drawing performance with deep learning[C]//International Conference on User Modeling,Adaptation,and Personalization.Cham:Springer,2015:216-227.
[27]PIECH C,BASSEN J,HUANG J,et al.Deep knowledge tracing[J].Computer science,2015,3(3):19-23.
[28]MIN W,FRANKOSKY M H,MOTT B W,et al.DeepStealth:leveraging deep learning models for stealth assessment in game-based learning environments[C]//Proceedings of the International Conference on Artificial Intelligence in Education.Cham:Springer,2015:277-286.
[29]XING W,DU D.Dropout Prediction in MOOCs:using deep learning for personalized intervention[J].Journal of educational computing research,2018,3:55-62.
[30]WANG W,YU H,MIAO C.Deep model for dropout prediction in MOOCs[C]//Proceedings of the 2nd International Conference on Crowd Science and Engineering.New York:ACM,2017:26-32.
[31]LIN F,LEI W,LIU S,et al.Classification of discussion threads in MOOC forums based on deep learning[C]//Proceedings of the 20172nd International Conference on Wireless Communication and Network Engineering(WCNE 2017).Lancaster:DEStech Publications,2017:493-498.
[32]WEI X,LIN H,YANG L,et al.A convolution-lstm-based deep neural network for cross-domain MOOC forum post classification[J].Information,2017,8(3):1-16.
[33]TANG S,PETERSON J C,PARDOS Z A.Modelling student behavior using granular large scaleaction data from a MOOC[EB/OL].(2016-08-16)[2018-06-16].https://arxiv.org/pdf/1608.04789.pdf
[34]寇媛媛.基于深度学习的在线教学推荐系统设计与研究[J].西安职业技术学院学报,2017(3):11-14.
[35]HASSAN H A M.Personalized research paper recommendation using deep learning[C]//Proceedings of the 25th Conference on User Modeling,Adaptation and Personalization.New York:ACM,2017:327-330.
[36]刘瑞梅,孟祥增.基于深度学习的多媒体画面情感分析[J].电化教育研究,2018,39(1):68-74.
[37]陈嘉华.基于深度学习的英语语音识别与发音质量评价[D].广州:广东外语外贸大学,2015.
[38]陈珊珊.自动作文评分模型及方法研究[D].哈尔滨:哈尔滨工业大学,2017.
[39]陈一乐.基于回归分析的中文作文自动评分技术研究[D].哈尔滨:哈尔滨工业大学,2016.
[40]刘明杨.高考作文自动评分关键技术研究[D].哈尔滨:哈尔滨工业大学,2015.
[41]李磊.基于机器学习的中小学数学自动阅卷系统研究与实现[D].桂林:广西师范大学,2017.
[42]刘逸雪,卢雨轩,丁亮,等.基于Bi-LSTM的数学主观题自动阅卷方法[J].管理观察,2018(2):109-113.
[43]汪张龙.人工智能技术在考试中的应用[J].中国考试,2017(11):30-36.
[44]YU X,YU H,TIAN X Y,et al.Recognition of college students from Weibo with deep neural networks[J].International journal of machine learning and cybernetics,2017,8(5):1447-1455.
[45]廖鹏,刘宸铭,苏航,等.基于深度学习的学生课堂异常行为检测与分析系统[J].电子世界,2018(8):97-98.
[46]曾志.基于人脸特征与深度学习的学生人格特质分析[D].南昌:江西师范大学,2017.
[47]黄蓓静.深度学习技术在中文人物关系抽取中的应用研究[D].上海:华东师范大学,2017.
[48]李依尘.面向自动问答的中学历史知识库构建[D].哈尔滨:哈尔滨工业大学,2017.