A new method for recommending learning paths based on skill context

WANG Ronghai; ZHI Huilai; ZHOU Yinfeng; LI Jinjin

doi:10.16152/j.cnki.xdxbzr.2026-03-013

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A new method for recommending learning paths based on skill context

更新时间：2026-07-07

- A new method for recommending learning paths based on skill context
- Journal of Northwest University (Natural Science Edition) Vol. 56, Issue 3, Pages: 608-617(2026)
- 作者机构：
  
  1.泉州师范学院　数学与计算机科学学院，福建　泉州　362000
  2.福建省大数据管理新技术与知识工程重点实验室，福建　泉州　362000
  3.陕西师范大学　数学与统计学院，陕西　西安　710119
  4.闽南师范大学　数学与统计学院，福建　漳州　363000
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2026-03-013
  CLC： TP182
- Received：30 January 2026，
  
  Revised：2026-02-10，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
WANG Ronghai, ZHI Huilai, ZHOU Yinfeng, et al. A new method for recommending learning paths based on skill context[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 608-617.
DOI：

WANG Ronghai, ZHI Huilai, ZHOU Yinfeng, et al. A new method for recommending learning paths based on skill context[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 608-617. DOI： 10.16152/j.cnki.xdxbzr.2026-03-013.

摘要

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.

关键词

Keywords

references

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