计算机科学 ›› 2025, Vol. 52 ›› Issue (4): 249-254.doi: 10.11896/jsjkx.240700129
肖鑫园, 唐九阳
XIAO Xinyuan, TANG Jiuyang
摘要： 在智能导学场景中,传统知识图谱的智能导学算法无法表征知识主题间的认知关系,且传统推荐方法缺少对学生和知识体系的动态交互设计,这两种问题会导致学生在学习过程中面临知识迷航。基于知识森林的多轮对话导学模型从知识森林模型的角度出发,提出一种涵盖知识前后序结构、知识中心度与难度,动态可交互的导学模型,并围绕中心度与先验知识定量评估了学生在学习过程中面临的知识迷航代价。为验证算法的有效性,对随机学习策略的知识迷航问题进行量化分析,在学科数据集GeoQSP,HisQSP,PhyQSP和DSAQSP上进行了多轮对话导学模型与随机学习策略的对比实验。实验结果表明,基于知识森林的多轮对话导学模型可以较大程度缓解因缺乏知识主题间的认知关系和动态交互能力导致的知识迷航问题。
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