数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (3): 94-110.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.03.008

doi: 10.11871/jfdc.issn.2096-742X.2025.03.008

• 专刊：中国科学院计算机网络信息中心成立30周年 • 上一篇    下一篇

学习技术演进视角下的大型组织机构学习管理系统建设与实践研究

赵以霞1,*(),熊英1,王闰强1,刘杨2,陶奕湲1   

  1. 1.中国科学院计算机网络信息中心，北京 100083
    2.中国科学院，北京 100864
  • 收稿日期:2025-05-15 出版日期:2025-06-20 发布日期:2025-06-25
  • 通讯作者: *赵以霞（E-mail: zyx@cnic.cn）
  • 作者简介:赵以霞，中国科学院计算机网络信息中心，博士，高级工程师，主要研究方向为在线学习、学习模式、推荐系统等。
    本文承担工作为：论文整体设计与内容撰写。
    ZHAO Yixia, Ph.D., is a senior engineer at Computer Network Information Center, Chinese Academy of Sciences. Her main research interests are online learning, learning mode, and recommender systems.
    In this paper, she is mainly responsible for overall design and content writing.
    E-mail: zyx@cnic.cn
  • 基金资助:
    数字中科院——数字化院继续教育体系建设工程(CAS-WX2022GC-0303)

Development and Practice of Learning Management System in Large-Scale Organizations: A Technological Evolution Perspective

ZHAO Yixia1,*(),XIONG Ying1,WANG Runqiang1,LIU Yang2,TAO Yiyuan1   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2. Chinese Academy of Sciences, Beijing 100864, China
  • Received:2025-05-15 Online:2025-06-20 Published:2025-06-25

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摘要：

【目的】分析在线学习各发展阶段的技术与模式特征，结合自主研发平台的应用实践，探讨其创新价值。【方法】通过文献梳理、案例比较及平台技术解析，总结在线学习从CAI（计算机辅助教学）到AI赋能阶段的演变路径，并基于自主研发平台验证其技术适配性。【结果】技术演进从CAI的单一多媒体交互发展为AI驱动的自适应学习，平台技术逐步实现多终端融合（如响应式设计、跨平台支持），内容生产趋向碎片化与个性化（如微课、智能推荐），学习理论从行为主义转向以学习者为中心的建构主义。自主研发平台在多终端适配、智能运营及个性化学习路径设计上展现优势，适应科研机构终身学习需求。【结论】在线学习正向智能化与社交化发展，技术与终身学习深度融合是核心趋势，自主研发平台通过技术创新为行业提供了高效解决方案。

关键词: 在线学习, 技术演进, 多终端融合, 个性化学习, AI赋能

Abstract:

[Objective] This paper is to analyze the technological and modal characteristics of online learning across its developmental stages and evaluate the innovative value of a self-developed platform. [Methods] Through literature review, case comparison, and technical analysis of platforms, the study traces the evolution of online learning from Computer-Assisted Instruction (CAI) to AI-enhanced stages, validating technical adaptability via a self-developed platform. [Results] Technological evolution has advanced from the single-medium interaction of CAI to AI-driven adaptive learning, with platform technologies achieving multi-device integration (e.g., responsive design, cross-platform compatibility). Content production has shifted toward micro-learning modules and personalized recommendations, while learning theories transitioned from behaviorism to learner-centered constructivism. The self-developed platform demonstrates advantages in multi-device adaptability, intelligent operations, and personalized learning pathways, addressing lifelong learning demands. [Conclusions] Online learning is advancing toward intelligence and social interactivity, with technology-education integration as a main trend. The self-developed platform provides an efficient solution for the industry through technical innovation.

Key words: online learning, technological evolution, multi-device integration, personalized learning, AI-enhanced education