摘要
案例教学法切实回应了高等教育的实践性要求,情境化教学则为学生深度学习创造条件,使案例教学升级为课堂上的实验课。根据“问题呈现—知识表征—结果生成”规律,将体验式学习理论逻辑及策略引入案例教学中,构建案例教学情境化之四重结构,论证其具有激活学习动机、支持知识建构、促进能力养成和伦理塑造的功能,契合深度学习之需要。基于此,提出智能训练、合作学习和对抗情境的创设方案,并针对过程控制提出建议:教师应开展以“假设与证据”为特质的课堂论证和有效的课堂话语互动,克服大班教学中合作学习的异化,建立促进深度学习的学习评价体系等,以确保案例情境化教学与预期学习成果相适,达到深度学习之目的。
The case teaching method effectively responds to the practical nature of legal education,the situational transformation creates conditions for students’ deep learning and upgrades case teaching to an experimental class in the legal classroom.This paper introduces the logic and strategies of experiential learning theory into case teaching and builds a fourfold structure of the situational teaching of legal cases based on the law of “problem presentation-knowledge representation-result generation”,which proves that it has the functions of activating learning motivation,supporting knowledge construction,promoting ability formation and ethical shaping,and is suitable for the needs of deep learning.To ensure the appropriateness of the situational cases teaching to the expected learning outcomes and to achieve the purpose of deep learning,it proposes the creation of intelligent training situations,cooperative learning situations and confrontation situations,and suggests that teachers should carry out classroom argumentation with the characteristics of “hypothesis and evidence”,develop effective classroom discourse interaction,and overcome the alienation of cooperative learning in large classes and establish a learning assessment system that promotes deep learning,to ensure situational teaching to be compatible to the desired learning achievements,thus achieving deep learning purposes.
作者
彭光明
PENG Guangming(School of Humanities and Law,Hebei University of Science&Technology,Shijiazhuang 050091,China)
出处
《创新与创业教育》
2022年第5期106-112,共7页
Journal of Innovation and Entrepreneurship Education
基金
2019年河北省专业学位教学案例库建设项目“《国际经济法学》研究生教学案例库建设”
河北科技大学教育教学改革研究重点项目“基于OBE理念的法学专业课程群建设研究”(2019-ZDB12)。
关键词
案例教学
情境化教学
深度学习
过程控制
case teaching
situational teaching
deep learning
process controlling