摘要
人工智能对教学模式和流程的变革,推动了教育生态体系的创新,研究人工智能在职业教育教学环节担任的角色及它在未来智能教育的融合发展是当前研究的热点问题.其中,建构主义教学理论认为,人工智能对教育教学的影响可以借助教学情境的智能构建、让学生在沉浸式学习环境中完成知识的迁移与建构.本研究设计了基于CXM(Customer Experience Management,客户体验管理)的智能交互情境和职业能力建构个性化识别方式,建立了人工智能视域下基于建构主义理念的高职教学模式,研究了该模式下的智能反馈机制.研究结果表明,把人工智能技术及理念应用于高职教学,将教师的教学设计融入智能教学情境,能够更好地兼顾学生的学习兴趣,强化学生学习的过程"痕迹"管理,通过数据"痕迹"解读学生的"技能缺失点""薄弱点",进而智能推荐适合学生"技能身份"的学习策略及路径,实现学生技能的自主迭代升级.
Abstract
The transformation of teaching models and processes driven by artificial intelligence has promoted innovation in educational ecosystem.The research on the role of artificial intelligence in vocational education and its integration and development in future intelligent education is a hot topic in current research.The constructivist teaching theory suggests that the impact of artificial intelligence on education and teaching can be achieved through the intelligent construction of teaching scenarios,allowing students to transfer and construct knowledge in an immersive learning environment.This research designs an intelligent interactive scenario based on CXM(Contextual Experience Management)and a personalized identification method for vocational skills construction,establishes a constructivism-based artificial intelligence vocational education teaching model and explores the intelligent feedback mechanism in vocational education teaching from the perspective of artificial intelligence.The research results indicate that applying artificial intelligence technology and concepts to vocational education and incorporating teachers'instructional design into intelligent teaching scenarios can better balance students'learning interests,strengthen the management of"traces"in students'learning process,interpret students'"skill gaps"and"weak points"through data"traces",and intelligently recommend learning strategies and paths suitable for students'"skill identity"so as to achieve autonomous iteration and upgrading of students'skills.
基金项目
四川省教育信息化与大数据中心四川省教育信息技术研究规划课题(十四五)(2021)(川教馆[2021]248)
四川省民办教育协会一般科研课题(2022)(MBXH22YB479)
NETL
NSTL
万方数据