摘要
如何实现从“知识占有”到“知识创造”的认知范式转变,仍是一个亟待揭示的黑箱。通过对20名“强基计划”学生的深度访谈发现,他们的认知范式转变表现为从“强知识惯性”向“构建型创新”的转变。其中,认知冲突并非导致认知范式转变的必然诱因,更为直接的驱动因素是高阶认知参与。受自我觉察和自我调节驱动,创新动机和元认知体验间接影响认知范式转变。此外,伴随反思性监控的循环,“强基计划”学生不断拓展新思路、实践新技能,实现了认知范式转变。鉴于此,高校可通过“场景式”培育模式等方式,激发“强基计划”学生对传统思维范式的颠覆精神,使其在突破性、革命性的科研创新实践中将真实世界与知识经验深度整合,最终实现转识成智、破旧立新。
How to realize the cognitive paradigm shift from“knowledge possession”to“knowledge creation”is still a black box that needs to be revealed.Through in-depth interviews with 20 students of the“Pilot Reform Program of Enrollment for Basic Disciplines”,it was found that their cognitive paradigm shift manifested itself in a shift from“strong knowledge inertia”to“constructive innovation”.Among them,cognitive conflict is not the inevitable cause of the paradigm shift;rather,the more direct driving force is higher-order cognitive engagement.Driven by self-awareness and self-regulation,motivation for innovation and metacognitive experience indirectly influence the cognitive paradigm shift.Along with the cyclical process of reflective monitoring,students in the“Pilot Reform Program of Enrollment for Basic Disciplines”undergo a cognitive paradigm shift as they continuously expand their thinking and practice new skills.Therefore,universities can cultivate a disruptive mindset in“Pilot Reform Program of Enrollment for Basic Disciplines”students towards traditional paradigms through approaches such as a“scenario-based”training model.This approach allows students to integrate real-world experiences with theoretical knowledge in groundbreaking and revolutionary research practices,ultimately transforming knowledge into wisdom and fostering innovation that challenges established norms and paves the way for new paradigms.
作者
朱德玲
郭仕豪
余秀兰
ZHU Deling;GUO Shihao;YU Xiulan(Nanjing University,Nanjing 210023)
出处
《中国高教研究》
2025年第2期5-12,共8页
China Higher Education Research
关键词
强基计划
拔尖创新人才
认知范式
高阶思维
认知重构知识模型
Pilot Reform Program of Enrollment for Basic Disciplines
top-notch innovative talents
cognitive paradigm
higher-order thinking
cognitive reconstruction of knowledge model
