基于"双师课堂"的人工智能跨学科项目化教学模式构建及应用
Construction and Application of AI Interdisciplinary Project Teaching Model Based on "Dual-Teacher Classroom"
许月媚1
作者信息
- 1. 广州市南武中学,广东 广州 510220
- 折叠
摘要
从人类教师和虚拟数字人教师协同授课的"双师课堂"视角,赋予人工智能跨学科项目化教学模式新框架,帮助学生更好地适应教育数字化的发展,对国家创新人才培养具有较大的促进作用.人工智能课程涉及多学科知识,没有成熟的教学模式可供借鉴,给开展人工智能课程教学带来了诸多难题.本研究在梳理人工智能课程、跨学科学习、项目化学习内涵的基础上,依托普通高中信息技术课程标准、义务教育信息科技课程标准和知识建构理论,引入虚拟数字人教师,构建了基于"双师课堂"的人工智能跨学科项目化教学模式,其内涵包括真实项目活动设计、学生自主参与知识建构、"双师课堂"协同教学、学生跨学科能力培养、跨学科学习效果评价等.同时,应用该模式在中学人工智能课程中组织教学并形成系列教学案例,以期为研究者和教师开展人工智能课程教学提供借鉴.
Abstract
Based on the"dual-teacher classroom"mode,in which human teachers and virtual digital human teachers collaborate to teach,a new framework of AI interdisciplinary project teaching model is constructed to help students better adapt to the development of digital education,which is a great contribution to the cultivation of national innovative talents.Since AI courses involve multiple disciplines,and there is no mature teaching model for reference,there are many challenges for teaching such courses.Based on the connotation of AI curriculum,interdisciplinary learning and project learning,this study introduces virtual digital human teachers,and builds an AI interdisciplinary project teaching model based on"dual-teacher classroom"in view of the IT curriculum standards in general high school and compulsory education as well as the knowledge construction theory.The model includes the design of real project activities,students'independent participation in knowledge construction,collaborative teaching in"dual-teacher classroom",students'interdisciplinary competence development,and evaluation of interdisciplinary learning effects.Meanwhile,the model is applied in the secondary school AI course,and a series of teaching cases are developed to provide reference for researchers and teachers in teaching AI courses.
关键词
人工智能/"双师课堂"/跨学科学习/项目化学习/模式构建Key words
AI/"Dual-teacher classroom"/Interdisciplinary learning/Project learning/Model construction引用本文复制引用
基金项目
广州市教育科学规划课题(2023)(202214655)
出版年
2023
NETL
NSTL
万方数据