大数据视角下个性化自适应学习系统支撑模型及实现机制研究

发布时间：2018-06-06 13:40

本文选题：大数据 + 个性化自适应　；参考：《中国电化教育》2017年04期

【摘要】：随着云计算、物联网和移动互联技术的快速发展,我们由信息时代迈入了数据时代。尊重个体差异,促进个性化学习,是教育改革核心理念之一;个性化自适应学习,日益成为教育界关注的热点问题。基于以上背景,该文从大数据的视角出发,对个性化自适应学习系统的核心要素进行了分析:针对学习者需求的演进,分析了学习者的学习自主性、群体社会化和学习情感;面对学习资源的新诉求,探讨了资源的情境化和多维立体关联性;就系统架构的新特性,强调了系统生态化、数据采集精细化及系统自我修善与重生的必要性。通过以上对系统核心要素的分析,提出了基于大数据的个性化自适应系统架构模型,并从学习者模型、领域知识模型和自适应引擎三方面对其实现机制进行了探析,提出了基于多种群选择的学习路径推荐策略和基于加权协同过滤的学习资源推送方法。
[Abstract]:With the rapid development of cloud computing, Internet of things and mobile interconnection technology, we have entered the data age from the information age. Respecting individual differences and promoting individualized learning is one of the core concepts of educational reform, and individualized adaptive learning is becoming a hot issue in educational circles. Based on the above background, this paper analyzes the core elements of individualized adaptive learning system from the perspective of big data: according to the evolution of learners' needs, it analyzes learners' learning autonomy, group socialization and learning emotion; In the face of the new demands of learning resources, this paper probes into the situational and multidimensional relationship of resources, and emphasizes the necessity of system ecology, data collection refinement, self-improvement and rebirth of the system structure. Based on the above analysis of the core elements of the system, a personalized adaptive system architecture model based on big data is proposed, and its implementation mechanism is analyzed from three aspects: learner model, domain knowledge model and adaptive engine. A learning path recommendation strategy based on multi-group selection and a learning resource push method based on weighted collaborative filtering are proposed.
【作者单位】：东北师范大学教育学部;东北师范大学教育部数字化学习支撑技术工程研究中心;
【基金】：国家科技支撑计划“中小学师资培训服务支撑技术研究及应用示范项目”子课题“区域互动教研与培训学分交互应用示范”(课题编号:2014BAH22F05)阶段性成果
【分类号】：G434

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