Research on the mechanism and key technologies for big data collection in education

Huanyou CHAI; Sannyuya LIU; Lingyun KANG; Yaxian ZHANG; Qing LI; Zhi LIU

doi:10.11959/j.issn.2096-0271.2020051

您当前的位置：

首页 >

文章列表页 >

Research on the mechanism and key technologies for big data collection in education

TOPIC：EDUCATIONAL BIG DATA | 更新时间：2024-06-03

- Research on the mechanism and key technologies for big data collection in education
- Big Data Research Vol. 6, Issue 6, Pages: 2020051-1(2020)
- 作者机构：
  
  1. 华中师范大学国家数字化学习工程技术研究中心，湖北武汉 430079
  2. 华中师范大学教育大数据应用技术国家工程实验室，湖北武汉 430079
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2020051
  CLC： G-4
- Online First：2020-11，
  
  Published：15 November 2020
- 稿件说明：
移动端阅览
Huanyou CHAI, Sannyuya LIU, Lingyun KANG, et al. Research on the mechanism and key technologies for big data collection in education[J]. Big Data Research, 2020, 6(6): 2020051-1.
DOI：

Huanyou CHAI, Sannyuya LIU, Lingyun KANG, et al. Research on the mechanism and key technologies for big data collection in education[J]. Big Data Research, 2020, 6(6): 2020051-1. DOI： 10.11959/j.issn.2096-0271.2020051.

摘要

数据采集是实现教育大数据应用价值潜能的基础，因此对于教育大数据建设与应用至关重要。阐述了教育大数据的采集内容、采集方式、采集手段及标准与规范，并结合当前教育大数据建设与应用中的实际问题，分别从平衡数据共享与隐私保护、驱动数据治理与人才创新、创新采集机制与相关技术3个方面，对教育大数据采集研究提出对策与建议。

Abstract

The mechanism and its related technologies of data collection are the foundation of realizing the valuable potential of applying big data in education

and therefore are vital for the construction and application of big data in education.The contents

methods

means

standards and specifications of data collection of big data in education were outlined.Combining with the practical problems in the construction and application of big data in education

guidance on research on big data in education was provided

from the perspectives about balancing data sharing and privacy protection

driving data governance and talent acquisition

and improving the mechanisms and related technologies respectively.

关键词

Keywords

references

沈阳 , 田浩 , 曾海军 . 大数据时代的教育：若干认识与思考——访中国科学院院士梅宏教授 [J ] . 电化教育研究 , 2020 , 41 ( 7 ): 5 - 10 .

SHEN Y , TIAN H , ZENG H J . Education in the age of big data:some insights and reflections:an interview with professor MEI Hong,academician of Chinese Academy of Sciences [J ] . e-Education Research , 2020 , 41 ( 7 ): 5 - 10 .

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 .

YANG X M , TANG S S , LI J H . The definition,potential value and challenges of big data in education [J ] . Modern Distance Education Research , 2016 ( 1 ): 50 - 61 .

程玉 , 胡凡刚 , 吴运明 . 教育大数据价值体现、问题反思与发展路径 [J ] . 软件导刊 , 2020 , 19 ( 5 ): 281 - 284 .

CHENG Y , HU F G , WU Y M . Reflections on the values,problems and development path of big data on education [J ] . Software Guide , 2020 , 19 ( 5 ): 281 - 284 .

DANIEL B . Big data and analytics in higher education:opportunities and challenges [J ] . British Journal of Educational Technology , 2015 , 46 ( 5 ): 904 - 920 .

LI Y , ZHAI X . Review and prospect of modern education using big data [J ] . Procedia Computer Science , 2018 , 129 : 341 - 347 .

刘三女牙 , 杨宗凯 , 李卿 . 计算教育学：内涵与进路 [J ] . 教育研究 , 2020 , 41 ( 3 ): 152 - 159 .

LIU S N Y , YANG Z K , LI Q . Computational education:connotations and approaches [J ] . Educational Research , 2020 , 41 ( 3 ): 152 - 159 .

VATSALA V , JADHAV R R S . A review of big data analytics in sector of higher education [J ] . International Journal of Engineering Research and Applications , 2017 , 7 ( 6 ): 25 - 32 .

何普亮 , 张战胜 . 大数据时代的教育数据挖掘：方法、工具与应用 [J ] . 中国教育技术装备 , 2019 ( 23 ): 7 - 10 .

HE P L , ZHANG Z S . Educational data mining in big data era:methods,tools and applications [J ] . China Educational Technology ＆ Equipment , 2019 ( 23 ): 7 - 10 .

王小根 , 单必英 . 基于动态学习数据流的“伴随式评价”框架设计 [J ] . 电化教育研究 , 2020 , 41 ( 2 ): 60 - 67 .

WANG X G , SHAN B Y . Design of“Accompanying Evaluation” framework based on dynamic learning data flow [J ] . e-Education Research , 2020 , 41 ( 2 ): 60 - 67 .

AMAXILATIS D , AKRIVOPOULOS O , MYLONAS G , et al . An IoT-based solution for monitoring a fleet of educational buildings focusing on energy efficiency [J ] . Sensors , 2017 , 17 ( 10 ):2296.

徐岸峰 , 杨仲基 , 王波 . 基于智慧教育平台大数据的学生学习行为分析 [J ] . 高教学刊 , 2020 ( 22 ): 16 - 19 .

XU A F , YANG Z J , WANG B . Analysis of students’ learning behavior based on big data of intelligence education platform [J ] . Journal of Higher Education , 2020 ( 22 ): 16 - 19 .

BIHANI A , PAEPCKE A . QuanTyler:apportioning credit for student forum participation [C ] // The 11th International Conference on Educational Data Mining . New York:ACM Press , 2018 : 106 - 115 .

JANSEN B J . Search log analysis:what it is,what’s been done,how to do it [J ] . Library ＆ Information Science Research , 2006 , 28 ( 3 ): 407 - 432 .

JEFFRIES B , KOPRINSKA I , MCBROOM J , et al . Mining behaviours of students in AutoGrading submission system logs [M ] . Heidelberg : SpringerPress , 2016 .

CHARLAND A , LEROUX B . Mobile application development:web vs.native [J ] . ACM Queue , 2011 , 54 ( 5 ): 49 - 53 .

LI C , MA J . Research on online education teacher evaluation model based on opinion mining [C ] // The 2012 National Conference on Information Technology and Computer Science . Paris:Atlantis Press , 2012 : 1061 - 1064 .

ELLIOTT C , NEAL D . Evaluating the use of lecture capture using a revealed preference approach [J ] . Active Learning in Higher Education , 2016 , 17 ( 2 ): 153 - 167 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

PEI X L , WANG X , WANG Y F , et al . Internet of things based education:definition,benefits,and challenges [J ] . Applied Mechanics and Materials , 2013 , 411 : 2947 - 2951 .

KASSAB M , DEFRANCO J , LAPLANTE P . A systematic literature review on Internet of things in education:benefits and challenges [J ] . Journal of Computer Assisted Learning , 2020 , 36 ( 2 ): 115 - 127 .

COOK C , OLNEY A M , KELLY S , et al . An open vocabulary approach for estimating teacher use of authentic questions in classroom discourse [C ] // The 11th International Conference on Educational Data Mining . New York:ACM Press , 2018 : 116 - 126 .

MILLSA C , BIXLERA R , WANG X , et al . Automatic gaze-based detection of mind wandering during narrative film comprehension [C ] // The 9th International Conference on Educational Data Mining . New York:ACM Press , 2016 : 30 - 37 .

STEWART A , BOSCH N , MELLO S K D . Generalizability of face-based mind wandering detection across task contexts [C ] // The 10th International Conference on Educational Data Mining . New York:ACM Press , 2017 : 88 - 95 .

鲁鸣鸣 , 张丹 , 王建新 . 基于校园一卡通数据好友发现及应用 [J ] . 大数据 , 2017 , 3 ( 2 ): 78 - 91 .

LU M M , ZHANG D , WANG J X . Smartcard based campus friend mining and its applications [J ] . Big Data Research , 2017 , 3 ( 2 ): 78 - 91 .

DOLIANITI F S , IAKOVAKIS D , DIAS S B , et al . Sentiment analysis techniques and applications in education:a survey [C ] // The International Conference on Technology and Innovation in Learning,Teaching and Education . Heidelberg:Springer , 2019 : 412 - 427 .

WAMPFLER R , KLINGLER S , SOLENTHALER B , et al . Affective state prediction in a mobile setting using wearable biometric sensors and stylus [C ] // The 12th International Conference on Educational Data Mining . New York:ACM Press , 2019 : 198 - 207 .

VAIL A , WIGGINS J , GRAFSGAARD J , et al . The affective impact of tutor questions:predicting frustration and engagement [C ] // The 9th International Conference on Educational Data Mining . New York:ACM Press , 2016 : 247 - 254 .

刘三女牙 , 杨宗凯 , 李卿 . 教育数据伦理:大数据时代教育的新挑战 [J ] . 教育研究 , 2017 , 38 ( 4 ): 15 - 20 .

LIU S N Y , YANG Z K , LI Q . Education data ethic:the new challenge of education in big data era [J ] . Educational Research , 2017 , 38 ( 4 ): 15 - 20 .

DANIEL B K . Big data and data science:a critical review of issues for educational research [J ] . British Journal of Educational Technology , 2019 , 50 ( 1 ): 101 - 113 .

Views

935

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research and application practice of “Multi- Data Lake under One Middle-Platform” industrial big data platform based on Lakehouse technology

Research progress of government data identification technology and the next generation government data identification system

Technical architecture and implementation of decision-driven provincial government big data governance

Data central-platform: architecture and practice

Research on privacy protection mechanism and technology of educational big data

Related Author

JIANG Nan

Dahu XU

Wanzhe ZHANG

Wuai ZHOU

Xiaoliang SU

Yifeng GUO

Yun WANG

Jianhua FENG

Related Institution

China Petroleum & Chemical Corporation

College of Engineering, Peking University

School of Aerospace Engineering, Tsinghua University

China Mobile Information System Integration Co., Ltd.

Department of Computer Science and Technology, Tsinghua University

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273228 Visits Today：134

⁰