教育科学 ›› 2026, Vol. 42 ›› Issue (2): 47-53.

• 课程与教学研究 • 上一篇    下一篇

数智时代大学生自适应控制学习:现实困境、深层动因与优化路径*

关晓璐, 朱泓   

  1. 大连理工大学 高等教育研究院, 辽宁 大连 116024
  • 出版日期:2026-03-15 发布日期:2026-05-19
  • 作者简介:关晓璐(1991- ),女,辽宁大连人,大连理工大学高等教育研究院博士生,主要从事大学生学习发展研究;
    朱泓(1963- ),女,江苏江阴人,大连理工大学高等教育研究院研究员,博士生导师,主要从事大学课程教学、教育评估研究。
  • 基金资助:
    *教育部人文社会科学研究青年基金项目“高校思想政治教育数字叙事体系建构研究”(24YJC710052)

University Students’ Adaptive Control Learning in the Digital Intelligence Era: Practical Dilemmas, Underlying Causes, and Optimization Pathways

Guan Xiaolu, Zhu Hong   

  1. Graduate School of Education, Dalian University of Technology, Dalian Liaoning 116024, China
  • Online:2026-03-15 Published:2026-05-19

摘要:

在数智化转型推动教育范式重构的背景下,高等教育教学模式变革对大学生学习提出了新的挑战。大学生如何适应数智技术迭代发展、动态调控学习模式以提升学习成效,已成为学界关注的重要议题。当前,我国大学生在学习主动性、人际交往能力、学习持续性与自我调控能力等方面,与数智时代的发展要求存在明显差距。本研究以自适应控制理论为分析框架,从学习目标、学习策略、成果评估与过程调控四个维度剖析困境生成的动因,并针对性提出四项实施策略:确立产出导向的分层学习目标、构建人机协同的个性化学习模式、完善多元主体参与的评估体系、建立实时动态的反馈调节机制,以期实现数智时代大学生人机协同共生的自适应控制学习。

关键词: 数智时代, 大学生, 自适应控制学习, 学习模式

Abstract:

Against the backdrop of digital and intelligent transformation reshaping educational paradigms, changes in higher education teaching models have posed new challenges to university students’ learning. How university students adapt to the iterative development of digital and intelligent technologies and dynamically regulate their learning model to improve learning outcomes has become an important issue in academic research. Currently, there remains a clear gap between Chinese university students’ learning initiative, interpersonal communication ability, learning persistence, and self-regulation ability and the developmental requirements of the digital and intelligent era. Taking adaptive control theory as the analytical framework, this study examines the underlying causes of these dilemmas from four dimensions: learning objectives, learning strategies, outcome evaluation, and process regulation. It further proposes four targeted implementation strategies: establishing outcome-oriented and hierarchical learning objectives, constructing a personalized learning model based on human-machine collaboration, improving an evaluation system involving multiple subjects, and establishing a real-time dynamic feedback and regulation mechanism. These strategies aim to promote university students’ adaptive control learning characterized by human-machine collaborative symbiosis in the digital and intelligent era.

Key words: digital and intelligent era, university students, adaptive control learning, learning model

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