Yu Yue, Shafto Patrick, Bonawitz Elizabeth, Yang Scott C-H, Golinkoff Roberta M, Corriveau Kathleen H, Hirsh-Pasek Kathy, Xu Fei
Department of Psychology, Rutgers University-Newark, Newark, NJ, United States.
Department of Mathematics and Computer Science, Rutgers University-Newark, Newark, NJ, United States.
Front Psychol. 2018 Jul 17;9:1152. doi: 10.3389/fpsyg.2018.01152. eCollection 2018.
For infants and young children, learning takes place all the time and everywhere. How children learn best both in and out of school has been a long-standing topic of debate in education, cognitive development, and cognitive science. Recently, has been proposed as an integrative approach for thinking about learning as a child-led, adult-assisted playful activity. The interactive and dynamic nature of guided play presents theoretical and methodological challenges and opportunities. Drawing upon research from multiple disciplines, we discuss the integration of cutting-edge computational modeling and data science tools to address some of these challenges, and highlight avenues toward an empirically grounded, computationally precise and ecologically valid framework of guided play in early education.
对于婴幼儿来说,学习随时随地都在发生。儿童在学校内外如何才能学得最好,一直是教育、认知发展和认知科学领域长期争论的话题。最近,有人提出将引导式游戏作为一种综合方法,把学习视为由儿童主导、成人协助的趣味性活动。引导式游戏的互动性和动态性带来了理论和方法上的挑战与机遇。借鉴多学科的研究成果,我们讨论如何整合前沿的计算建模和数据科学工具来应对其中一些挑战,并强调构建一个基于实证、计算精确且生态有效的早期教育引导式游戏框架的途径。