【作 者】：
黄 琰， 赵呈领， 赵 刚， 刘 军， 疏凤芳， 李红霞
【关 键 词 】：
教育过程； 智能技术； 教育过程挖掘； 教育大数据
【栏 目】：
理论探讨
【中文摘要】：
在指数增长的教育大数据中，挖掘抽象复杂的教育过程规律需要智能技术的加持。教育过程挖掘是通过建立完整、紧凑的教育过程模型，揭示教学活动规律，优化教育实践的一种融合数据科学和过程科学的智能技术与方法。教育过程挖掘以研究框架为指导，可采用过程模型发现、一致性检验和过程模型增强三类场景中的关键技术和常用模型开展研究与实践，助力发现学习行为模式、预测学习效果趋势、改进教学评价反馈、提供教学决策支持和提升教育管理服务。教育过程挖掘发展亟须增强决策推荐与理论引领作用、探索深层次与多模态数据应用、优化算法以开发教育领域工具以及在各类新兴教育领域进行推广与创新。
【英文摘要】：
In the exponential growth of big data in education, the mining of the abstract and complex education process requires the support of intelligent technology. Education process mining is a kind of intelligent technology and method integrating data science and process science by establishing a complete and compact education process model, revealing the law of teaching activities, and optimizing education practice. Based on the guidance of the research framework, education process mining can use the key technologies, such as process model discovery, consistency checking and process model enhancement, and common models in the three scenarios to conduct research and practice, so as to discover learning behavior patterns, predict learning effect trends, improve teaching evaluation feedback, provide teaching decision support and improve education management services. It is necessary to strengthen the role of decision-making recommendation and theoretical guidance, explore deep and multimodal data application, optimize algorithms to develop tools in the field of education, and promote and innovate in various emerging education fields.