[1]余明华,冯 翔,祝智庭.人工智能视域下机器学习的教育应用与创新探索[J].远程教育杂志,2017,(03):011-21.[doi:10.15881/j.cnki.cn33-1304/g4.2017.03.002]
 Yu Minghua,Feng Xiang,Zhu Zhiting.The Educational Applications and Innovative Explorations of Machine Learning in the view of Artificial Intelligence[J].Distance Education Journal,2017,(03):011-21.[doi:10.15881/j.cnki.cn33-1304/g4.2017.03.002]
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人工智能视域下机器学习的教育应用与创新探索()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2017年03期
页码:
011-21
栏目:
前沿探索
出版日期:
2017-05-15

文章信息/Info

Title:
The Educational Applications and Innovative Explorations of Machine Learning in the view of Artificial Intelligence
作者:
余明华; 冯 翔; 祝智庭
1.华东师范大学 教育信息技术学系,上海 200062;2.华东师范大学 上海数字化教育装备工程技术研究中心,上海 200062;3.华东师范大学 开放教育学院,上海 200062
Author(s):
Yu Minghua; Feng Xiang; Zhu Zhiting
1.Department of Education Information Technology, East China Normal University, Shanghai 200062;2.Shanghai Engineering Research Center of Digital Education Equipment, East China Normal University, Shanghai 200062;3.Open Education College, East China Normal University, Shanghai 200062
关键词:
机器学习智慧教育人工智能个性化学习教育大数据教育创新
Keywords:
Machine learning Smart education Artificial intelligence Personalized learning Educational big data Educational innovation
分类号:
G420
DOI:
10.15881/j.cnki.cn33-1304/g4.2017.03.002
文献标志码:
A
摘要:
新技术带来的教育变革方兴未艾,人工智能与智慧教育引领教育教学的创新,已经成为教育信息化发展的必然趋势。随着教育大数据的崛起,如何对大量数据进行分析支持精准预测,是人工智能时代面对的一个新课题。机器学习作为人工智能的一个重要分支,能够满足教育大数据分析预测的需求。为此,基于“为何分析、分析什么、以何分析、何以应用”一系列问题,通过对机器学习的作用对象、作用过程、具体方法和利益相关者等方面的分析,探讨了机器学习和智慧教育的适切性。结合对近年来国外基于真实数据的机器学习教育应用案例研究成果的梳理和归纳,发现目前机器学习教育应用主要集中在学生建模、学生行为建模、预测学习行为、预警失学风险、学习支持和评测和资源推荐等六大方面。从跨界、技术和教学三个层面出发,基于智慧教育的框架对机器学习的教育应用与创新提出了相关建议。
Abstract:
The educational reform which is brought by new technology, is more obvious than before. Artificial intelligence and Smart education leading the educational innovative development has become the inevitable trend of educational informatization. With the rise of educational big data, how to analysis the data for predicting accurately, is the new challenge in artificial intelligence era. Machine learning, as a branch of artificial intelligence, can meet the need of analysis and prediction of smart education. For this purpose, based on a series of problems including “why, what, how to analysis, and how to apply” as the main line,discussthe propriety between machine learning and smart education from the aspects ofapproach objects, process, methods and stakeholders. Then through combed and concluded the abroad case studiesof machine learning educational application with real datain recent year, find that studies focus on six aspects including student modeling, student behavior modeling, predicting learning behavior, predicting student dropout, learning supporting and assessment, and resource recommendations. And from the view of interdisciplinary, technology, and pedagogy, propose some suggestions for the machine learning educational applications and innovation based on smart education framework.

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备注/Memo

备注/Memo:
基金项目:本文系全国教育科学“十二五”规划2014年度国家一般课题“智慧教育环境的构建与应用研究”(项目编号:BCA140051);2017年度教育部在线教育研究基金(全通教育)课题“在线教育系统中学生反馈文本的情感分析技术与应用研究”(项目编号:2017YB126)的研究成果。
更新日期/Last Update: 2017-05-15