11,739 | 220 | 5 |
下载次数 | 被引频次 | 阅读次数 |
智能技术赋能为教育评价变革发展提供了重要机遇,促进智能技术与教育评价融合创新是深化新时代教育评价改革的必然选择。据此,对智能技术赋能教育评价进行了系统研究,其历史渊源可追溯到管理工程领域的智能综合评价和教育评价领域计算机技术的应用。本质是基于智能技术对传统教育评价的突破与创新,通过解构、重构形成新的教育评价模式,具有科学化、多元化、立体化、最优化、精准化等主要特征。助力教育评价的智能技术及应用由"5+1"的总体框架(基础层、技术层、平台层、应用层、用户层5个层次结构和1个保障体系)构成。通过智能技术赋能"四个评价"的具体场景应用,采取理念引领、标准规范、主体关照、数据驱动、专业支持等整体推进策略,打造智能化教育评价生态体系。
Abstract:The enablement of intelligent technology provides an important opportunity for the reform and development of education evaluation, and promoting the integration and innovation of intelligent technology and educational evaluation is the inevitable choice to deepen the reform of education evaluation in the new era. Accordingly, the intelligent technology enabled evaluation education is studied systematically, and its historical origin can be traced back to the intelligent comprehensive evaluation in the field of management engineering and the application of computer technology in the field of education evaluation. Its essence is the breakthrough and innovation of traditional education evaluation based on intelligent technology, and the formation of a new education evaluation model through deconstruction and reconstruction, which has the main characteristics of scientificalization, diversification, three-dimensional, optimization and precision. The intelligent technology and its application to assist education evaluation consists of the overall framework of “5+1”(five hierarchical structures of basic layer, technology layer, platform layer, application layer and user layer and one guarantee system). Through the specific scene application of “four evaluations” enabled by intelligent technology, the overall promotion strategies of concept guidance, standard norms, subject care, data-driven and professional support are adopted to build an ecological system of intelligent education evaluation.
[1]杨勇.智能化综合评价理论与方法研究[D].杭州:浙江工商大学,2014.
[2]张殿尉.智能化教育评价领域演进路径、研究热点与前沿的可视化分析[J].工业技术与职业教育,2020,18(1):110-114.
[3]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 1996[EB/OL].https://files.eric.ed.gov/fulltext/ED398899.pdf,2021-02-19.
[4]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 2000[EB/OL].https://files.eric.ed.gov/fulltext/ED444604.pdf,2021-02-19.
[5]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 2004[EB/OL].https://files.eric.ed.gov/fulltext/ED484046.pdf,2021-02-19.
[6]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 2010[EB/OL].https://files.eric.ed.gov/fulltext/ED512681.pdf,2021-02-19.
[7]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 2016[EB/OL].https://tech.ed.gov/files/2015/12/NETP16.pdf,2021-02-19.
[8]U.S.DEPARTMENT OF EDUCATION.The U.S.Department of education.National Education Technology Plan 2017[EB/OL].https://tech.ed.gov/files/2017/01/NETP17.pdf,2021-02-19.
[9]刘邦奇,吴晓如.中国智能教育发展报告[M].北京:人民教育出版社,2020.19-21.
[10]杨现民,田雪松.互联网+教育:中国基础教育大数据[M].北京:电子工业出版社,2016.147.
[11]董奇,赵广立.科技赋能教育评价改革时机已至[N].中国科学报,2021-01-12(07).
[12]Zhang Qingchen,Yang Laurence T.,et al.A survey on deep learning for big data[J].Inform Fusion,2018,(42):146-157.
[13]Wiley J,Hastings P,et al.Different approaches to assessing the quality of explanations following a multiple-document inquiry activity in science[J].International Journal of Artificial Intelligence in Education,2017,27(4):758-790.
[14]杨现民,顾佳妮等.“互联网+”时代数据驱动的教育评价体系构架与实践进展[J].浙江师范大学学报(社会科学版),2019,44(4):16-26.
[15]郑燕林,柳海民.大数据在美国教育评价中的应用路径分析[J].中国电化教育,2015,(7):25-31.
[16]曹培杰.以新思维新技术破解教育评价痛点[EB/OL].https://epaper.gmw.cn/gmrb/html/2019-12/10/nw.D110000gmrb_20191210_4-13.htm,2021-02-05.
[17]朱成晨,闫广芬.现代化与专业化:大数据时代教育评价的新技术推进逻辑[J].清华大学教育研究,2018,39(5):75-80.
[18]刘邦奇,王亚飞.智能教育:体系框架、核心技术平台构建与实施策略[J].中国电化教育,2019,(10):23-31.
[19]范国睿.教育评价改革需要新路向[N].郑州日报,2020-07-24(08).
[20]边玉芳,王烨晖.增值评价:学校办学质量评估的一种有效途径[J].教育学报,2013,9(1):43-48.
[21]王晓平,齐森等.美国学校“成长测量”的7种主要方法[J].中国考试,2018,(6):21-27.
[22]杨宗凯.利用信息技术促进教育教学评价改革创新[J].人民教育,2020,(21):30-32.
[23]张治,戚业国.基于大数据的多源多维综合素质评价模型的构建[J].中国电化教育,2017,(9):69-77+97.
[24]刘邦奇,张振超等.区域教育大数据发展参考框架[J].现代教育技术,2018,28(4):5-12.
[25]张娜.从对教育的评价到促进教育的评价--教育评价国际研究进展综述[J].基础教育,2017,(4):81-88.
基本信息:
DOI:
中图分类号:G434
引用信息:
[1]刘邦奇,袁婷婷,纪玉超等.智能技术赋能教育评价:内涵、总体框架与实践路径[J].中国电化教育,2021,No.415(08):16-24.
基金信息:
认知智能国家重点实验室2020年度智能教育开放课题重点课题“智能技术支持下的因材施教与教育治理”(项目编号:iED2020-Z003)阶段性研究成果