大学化学 >> 2025, Vol. 40 >> Issue (10): 23-32.doi: 10.12461/PKU.DXHX202411073

所属专题： 教育数字化推动高等化学教育改革

教学研究与改革 上一篇    下一篇

基于知识图谱的分析化学智慧教学探索

朱佩佩, 汪莉, 陈莉莉, 廖勋凡, 陈义旺   

  1. 江西师范大学化学与材料学院, 南昌 330022
  • 收稿日期:2024-11-20 录用日期:2025-01-15 发布日期:2025-04-25
  • 通讯作者: 廖勋凡, 陈义旺 E-mail:xfliao@jxnu.edu.cn;ywchen@ncu.edu.cn
  • 基金资助:
    江西省高等学校教学改革研究项目(JXJG-23-2-17, JXJG-22-2-1, JXJG-23-2-53)

Intelligent Teaching in Analytical Chemistry: An Exploration Based on Knowledge Graphs

Peipei Zhu, Li Wang, Lili Chen, Xunfan Liao, Yiwang Chen   

  1. College of Chemistry and Materials, Jiangxi Normal University, Nanchang 330022, China
  • Received:2024-11-20 Accepted:2025-01-15 Published:2025-04-25
  • Contact: Xunfan Liao, Yiwang Chen E-mail:xfliao@jxnu.edu.cn;ywchen@ncu.edu.cn

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摘要： 本文以分析化学课程为例，探讨了基于知识图谱的教学资源构建与智慧教学路径的探索与实践。研究表明，知识图谱可根据学生学习数据提供定制化学习路径和资源推荐，提升自主学习能力，并通过实时反馈支持教师精准干预，优化教学内容。可视化知识图谱优化了知识整合与教学设计，支持数据驱动的自适应学习，为教学改革提供新思路。

关键词: 知识图谱, 分析化学, 智慧教学, 自适应学习

Abstract: Using the Analytical Chemistry course as a case study, this paper investigates the construction of knowledge graph-based teaching resources and explores practical approaches for intelligent teaching implementation. The study demonstrates that knowledge graphs enable personalized learning pathways and tailored resource recommendations by analyzing student learning data, thereby enhancing self-directed learning capabilities. Furthermore, real-time feedback mechanisms facilitate targeted instructor interventions and content optimization. The visual representation of knowledge graphs improves knowledge integration and instructional design, enabling data-driven adaptive learning and offering innovative perspectives for educational reform.

Key words: Knowledge graph, Analytical chemistry, Intelligent teaching, Adaptive learning