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作 者:余胜泉 李晓庆 Yu Shengquan;Li Xiaoqing(Beijing Advanced.Innovation Center for Future Education,Beijing 100875)
机构地区:[1]北京师范大学未来教育高精尖创新中心,北京100875
出 处:《中国电化教育》2019年第1期17-27,共11页China Educational Technology
基 金:教育部哲学社会科学研究重大课题"‘互联网+’教育体系研究"(项目编号:16JZD043)阶段性研究成果
摘 要:教育大数据作为未来区域教育发展的契机和质量改进与提升的破解方法,在智慧教育领域有着不可估量的发展潜能。该文大数据与区域教育发展的关系出发,剖析了区域教育大数据的技术架构,包括教育过程多模态数据收集、学习者个性化认识模型构建、学科知识图谱构建、数据挖掘分析、资源语义标记与汇聚、个性化智能推荐引擎、区域教育决策分析等,提出构建区域大数据无缝流转的开放生态系统。同时,该研究以智能教育大数据公共服务平台"智慧学伴"为例,提出区域教育大数据应用模型,并展望了区域教育大数据未来发展的三个智能服务阶段:个性化定向学习支持的"学习助手"、封闭性问题解答和情感支持的"学习伙伴"、开放性问题解答和智慧成长支持的"学习导师"。该研究从理论构建、实践应用、未来展望三个层面对区域教育大数据作了较为全面的论述,以期为区域教育大数据的应用提供理论参考模型和实践指导。Big data Education, as an opportunity for future development of education in regions and a method for quality improvement, has immeasurable development potential in the field of intelligent education. From the perspective of the relationship between big data and region education development, this paper analyzes the technical architecture, which contains modal data collection of big data in regional education, personalized knowledge model building about learners, subject knowledge map construction, data mining and analysis, resource semantic markup and gathering, personalized intelligent recommendation engines, regional education decision analysis, etc., and put forward the construction of open ecosystem that regional big data can be transferred seamlessly. Meanwhile, Taking the intelligent education big data public service platform "smart learning partner" as an example, this study proposed the big data application model in region education, and make outlook about the three intelligent service stages about big data of region education in the future development: "learning assistant" with personalized directional learning support, "learning partner" with closed questions and emotional support, "learning mentor" with open questions and intelligent growth support. This study comprehensively discussed big data of region education from three aspects of theoretical construction, practical application and future prospect, expecting to provide theoretical reference model and practical guidance for the application of big data in region education.
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