【作 者】：
赵 刚， 初 洁， 朱文娟， 尹江华， 杨丽君， 蒋姝凡， 吴林静
【关 键 词 】：
知识图谱； 智能户外学习； 动态学习资源； 智能生成； 动态本体
【栏 目】：
学习环境与资源
【中文摘要】：
户外学习要求其学习资源具有碎片化、动态性与强交互等特点，而现有学习资源构建模式存在高成本、低效率、适应性差等问题，且缺乏系统性的知识结构，无法满足智能户外学习需求。因此，文章结合大数据和人工智能技术，提出了一种基于知识图谱的户外动态学习资源智能生成与服务模型，利用多种数据源信息构建户外学习领域知识图谱，基于推理规则智能获取户外学习场景的实体与属性关系，然后对网页上的关联资源进行动态提取与组织，生成多样化、轻量化、碎片化、交互性强的动态学习资源，为不同认知风格的学习者提供具有差异性的动态情境化学习内容。同时，对模型加以应用分析，研究结果显示，动态情境化的学习资源与传统网页资源在学习资源内容、结构和学习体验方面均存在显著性差异，且视觉型、听觉型与动觉型学习者在学习资源内容和结构方面差异更为显著。研究表明，基于知识图谱的户外动态学习资源能够满足不同类型学习者的智能户外学习需求，为智能户外学习资源开发研究提供了新路径。
【英文摘要】：
Outdoor learning requires its learning resources to be fragmented, dynamic and strongly interactive, while the existing learning resources are extremely constructed with high cost, low efficiency and poor adaptability, and lacks a systematic knowledge structure, which cannot meet the demand of intelligent outdoor learning. Therefore, this paper combines big data and artificial intelligence technology to propose an intelligent generation and service model of dynamic learning resource for outdoor learning based on knowledge mapping. It uses information from multiple data sources to construct a knowledge mapping of outdoor learning domain, and intelligently obtains entity and attribute relationships of outdoor learning scenes based on inference rules. Then the associated resources on the web page are dynamically extracted and organized to generate diverse, lightweight, fragmented and interactive dynamic learning resources, which provide dynamic contextualized learning contents with differences for learners of different cognitive styles. At the same time, the model is applied and analyzed. The results shows that there are significant differences between the dynamic contextualized learning resources and the traditional web resources in terms of learning resource content, structure and learning experience, and the differences in the content and structure of the learning resources are more significant for visual, auditory and kinesthetic learners. The study shows that the dynamic outdoor learning resources can meet the needs of intelligent outdoor learning for different types of learners, which provides a new path for intelligent outdoor learning resource development research.