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    数据驱动“知能发展”的微型学习系统 分布架构研究

    Research on Distributed Architecture of Micro-learning System for Data-driven Knowledge and Ability Developmen

【作      者】:

权国龙, 赵 春, 蔡慧英, 顾小清


【关 键 词 】:

教育信息化2.0; 教育数据; 知能图谱; 微型学习系统; 分布式共建共享


【栏      目】:

学习环境与资源


【中文摘要】:

创新驱动发展战略下“知能发展”是创新人才培养的重要目标。在技术融合应用中,可以充分利用教育数据促进主体“知能发展”以发挥其支持创新人才培养、驱动教育创新发展的作用。在教育系统全景中考虑驱动“知能发展”的技术形态和技术架构,在分布式格局中思考系统化建设的技术途径,并通过教育数据的技术设计支持主体“知能发展”图景,可以最大化教育数据的“使能创新”作用。基于文献调研和实践展开分析,在信息与数据教育应用的理性逻辑和实践经验间寻求技术结合点,可为数据驱动主体“知能发展”的系统建设提供建设性方案。研究认为,联结教育数据的表征本相和教育系统活动特征,以“知能图谱”和微型化学习系统为架构基础,将基于微型系统的分布式架构置于中心地位,实行数据驱动的微型化“知能”训练模式,可以通过可定位、可连续、可精准的学习历程促进主体“知能发展”,支持科创人才培养。


【英文摘要】:

Under the strategy of innovation-driven development, "knowledge and ability development" is an important goal for the cultivation of innovative talents. In the application of technology, educational data can be fully utilized to promote the development of people's knowledge and ability, so as to play its role in supporting the cultivation of innovative talents and driving the innovation and development of education. The "enabling innovation" effect of educational data can be maximized if the technical form and architecture driving the development of knowledge and ability are considered in a panoramic view of the education system, the technical approach of systematic construction is speculated in distributed pattern, and the development of the agent's knowledge and ability is supported through the technical design of education data. Based on literature research and practice analysis, seeking the technical combination between the rational logic and practical experience of the educational application of information and data can provide a constructive plan for the system construction of data-driven development of the agent's knowledge and ability . The study suggests that the characterization of educational data and the characteristics of educational system activities can be linked. Based on the "knowledge and ability map" and the micro learning system, the distributed architecture can be placed in the center, and the data-driven miniaturized "knowledge and ability" training mode can be implemented. Then, the development of the agent's knowledge and ability can be promoted and the cultivation of scientific and innovative talents can be supported through the locatable, continuous, and accurate learning process.

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