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形式概念分析为构建知识结构和寻找学习路径提供了强有力的工具。现有的基于技能背景推荐学习路径的方法需要遍历整个知识结构,不利于在大数据环境下推荐学习路径。针对此问题,该文在技能背景下,分别基于合取模型和析取模型提出了新的学习路径推荐方法。首先,分别在合取模型和析取模型下定义了知识状态关于技能的边缘。其次,分别在两种模型下基于知识状态关于技能的边缘给出推荐学习路径的方法。最后,通过在UCI的3个数据集下的实验验证所提出方法的有效性。通过该方法无需构建知识结构就能实现个性化推荐学习路径,弥补了现有方法的不足。
Abstract：Formal concept analysis provides powerful tools for constructing knowledge structures and finding learning paths.However,existing methods for recommending learning paths based on skill context require traversing the entire knowledge structure,which is inefficient in a big data environment.To address this issue,this paper proposes new methods for recommending learning paths based on skill context using both conjunctive and disjunctive models.First,the concept of the fringe of knowledge states regarding skills is defined under both models.Next,methods for recommending learning paths are presented based on these fringes.Finally,experiments conducted on three UCI datasets demonstrate the effectiveness of the proposed methods.By using the methods presented in this paper,personalized learning path recommendations can be achieved without constructing the entire knowledge structure,thus overcoming the limitations of existing methods.
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基本信息:
中图分类号:TP391.3
引用信息:
[1]王荣海,智慧来,周银凤,等.技能背景下推荐学习路径的新方法[J].西北大学学报(自然科学版)().
基金信息:
国家自然科学基金(12271191); 福建省自然科学基金(2024J01793)
2025-07-28
2025-07-28
2025-07-28