[1]陈恩红,刘淇,王士进,等.面向智能教育的自适应学习关键技术与应用[J].智能系统学报,2021,16(5):886-898.[doi:10.11992/tis.202105036]
CHEN Enhong,LIU Qi,WANG Shijin,et al.Key techniques and application of intelligent education oriented adaptive learning[J].CAAI Transactions on Intelligent Systems,2021,16(5):886-898.[doi:10.11992/tis.202105036]
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第5期
页码:
886-898
栏目:
吴文俊人工智能科技进步奖一等奖
出版日期:
2021-09-05
- Title:
-
Key techniques and application of intelligent education oriented adaptive learning
- 作者:
-
陈恩红1, 刘淇1, 王士进2, 黄振亚1, 苏喻1,2, 丁鹏2, 马建辉1, 竺博2
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1. 中国科学技术大学 计算机科学与技术学院 大数据分析与应用安徽省重点实验室,安徽 合肥 230027;
2. 科大讯飞股份有限公司,安徽 合肥 230088
- Author(s):
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CHEN Enhong1, LIU Qi1, WANG Shijin2, HUANG Zhenya1, SU Yu1,2, DING Peng2, MA Jianhui1, ZHU Bo2
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1. Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
2. iFLYTEK Co., Ltd, Hefei 230088, China
-
- 关键词:
-
自适应学习; 智能教育; 教学资源表示; 质量评估; 内容检索; 认知诊断; 知识追踪; 个性化推荐; 自适应推荐; 智能教育系统
- Keywords:
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adaptive learning; intelligent education; teaching resources representation; quality evaluation; content retrieval; cognitive diagnosis; knowledge tracing; personalized recommendation; adaptive recommendation; intelligent education system
- 分类号:
-
TP18
- DOI:
-
10.11992/tis.202105036
- 摘要:
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本文是关于我们获得2020年度吴文俊人工智能科学技术奖主要工作的一个介绍。该成果针对自适应学习中面临的教学资源表示困难、学习状态诊断困难以及学习策略设计困难等关键技术难题,首先构建数据驱动的教学资源无监督表示新框架,提高了教学资源质量评估和内容检索的精度和效率。其次提出基于深度学习的学习者认知诊断新方法,突破了以量表为基础的教育测量理论研究范式。然后设计基于知识匹配的个性化推荐技术以及多目标匹配的自适应推荐技术,满足了智能教育场景的复杂约束与学习者的多样目标需求。最后,本文成果研发了面向基础教育的智能教育系统——智学网,已在全国推广使用,对我国智能教育发展具有积极意义。
- Abstract:
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This paper is an introduction to our main work in winning of the 10th Wu Wenjun Artificial Intelligence Science and Technology Award. In response to key technical problems in adaptive learning, such as the difficulties in the representation of teaching resources, the diagnosis of knowledge state and the design of learning strategies, we first constructed a new framework for data-driven unsupervised representation of teaching resources, which significantly improves the accuracy and efficiency of quality evaluation and content retrieval of teaching resources. Then proposed a new method for learner cognitive diagnosis based on deep learning, which breaks through the scale-based research paradigm of educational measurement theories. And further designed a personalized recommendation technology based on knowledge matching and an adaptive recommendation technology based on multi-objective matching, meeting the complex constraints of intelligent education scenarios and the diverse learning targets of learners. Finally, an intelligent education system— “Zhixue.com” was developed for basic education, which has been popularized and used throughout the country. This system plays a positive role in the development of China’s intelligent education.
备注/Memo
收稿日期:2021-05-25。
基金项目:国家自然科学基金项目(61922073,U20A20229,61727809)
作者简介:陈恩红,教授,博士生导师,IEEE高级会员、CAAI会士、CCF会士、大数据专家委员会副主任,主要研究方向为机器学习与数据挖掘、社会网络、个性化推荐。国家自然科学基金重大科研仪器研制项目、联合基金重点项目、国家863计划、科技部重点研发计划课题等多项。发表学术论文150余篇;刘淇,教授,博士生导师,主要研究方向为数据挖掘与知识发现、机器学习方法及其应用、教育大数据分析。入选中国科协“青年人才托举工程”、CCF青年人才托举计划(2017年)、微软亚洲研究院青年学者“铸星计划”、CCF-Intel青年学者提升计划等。主持国家基金面上项目,科技部重点研发计划课题等多项。发表学术论文80余篇;王士进,高级工程师,主要研究方向为人工智能、模式识别、智能教育系统。主持和参与863计划重点项目、工信部电子信息产业发展基金项目等多项,获授权专利和软件著作权10余项。发表学术论文30余篇。
通讯作者:刘淇.E-mail:qiliuql@ustc.edu.cn
更新日期/Last Update:
1900-01-01