摘要
针对大学英语翻译教学方式单一枯燥、课堂缺乏互动、教学氛围沉闷等问题,基于支架理论,采用多模态智慧课堂与大学英语翻译课堂进行深度融合,以期在课时不变甚至减少的情况下,探索有效的大学英语翻译课堂教学模式。支架理论引导学生在课前、课间与课后通过自主学习、协同合作来获取知识。多模态智慧课堂教学在调动学生多种感官的同时,利用高度发达的现代信息网络,实现了课上、课下人机结合和优势互补,有助于改进支架理论的教学效果,并且帮助师生构建新型的大学英语翻译课堂教学文化,培养学生的跨文化素养。基于支架理论的多模态智慧教学,不仅优化了大学英语翻译课堂教学,还为大学英语翻译教学改革提供了新的视角。
In view of the problems such as monotonous teaching methods, lack of interaction in the classroom, and dull teaching atmosphere in college English translation teaching, a deep integration is taken between the multimodal intelligent classroom and college English translation classroom based on scaffolding theory, so as to explore the effective classroom teaching mode of college English translation and improve the quality of translation teaching while the existing class hours remain unchanged or even reduced. Scaffolding theory guides students to acquire knowledge through autonomous learning and collaborative cooperation before, during and after class. The multimodal intelligent classroom teaching mobilizes students’ multiple senses while using the highly-developed modern information network to realize the combination of human-machine and complementary advantages in and out of class, which helps to improve the teaching effect of scaffolding theory, build a new culture of college English translation teaching and learning, and cultivate students’ cross-cultural literacy. The multimodal intelligent teaching based on scaffolding theory not only optimizes college English translation teaching, but also provides a new perspective for college English translation teaching reform.
作者
周天楠
白雪晴
ZHOU Tiannan;BAI Xueqing(School of Foreign Languages,Northeast Petroleum University,Daqing,Heilongjiang 163318,China)
出处
《成都师范学院学报》
2022年第12期80-87,共8页
Journal of Chengdu Normal University
基金
2022年度黑龙江省教育科学“十四五”规划重点课题“多模态及支架式理论在大学英语翻译教学中的应用研究”(GJB1422171)
黑龙江省本科高校首批外语教育改革创新项目“基于数字布鲁姆的大学英语智慧教学研究”(HWX2022037-C)。
关键词
智慧教学
大学英语翻译
建构主义
支架教学
支架理论
人机结合
自主学习
intelligent teaching
college English translation
constructivism
scaffolding instruction
scaffolding theory
human-machine combination
autonomous learning