SL-CN: Guiding guided learning: Developmental, educational and computational perspectives

这个学习科学协作网络汇集了来自罗格斯大学、坦普尔大学、波士顿大学、加州大学伯克利分校和特拉华大学的心理学、教育学和计算机科学研究人员，专注于儿童如何学习得最好的问题。该网络将研究如何将直接教学与探究性学习相结合，以促进立即和长期的学习。以前的一些研究表明，直接教学是最好的，但其他研究表明，探究性学习更好。关于哪种方法更好的争论反映出，缺乏一种理论来说明如何将这些方法结合起来，以产生比单独使用其中任何一种方法更好的学习结果。本项目的研究部分将结合这些方法的要素来发展指导学习的理论。这项研究将通过发展新的教育方法和思维方式来为教育政策提供信息，促进即时学习，同时促进参与和享受，从而推动未来的学习。教育和认知发展方面的学生将学习方法的选择，如直接教学和探索性游戏，视为一个非此即彼的问题。目前的研究旨在整合这些方法的优点，并将其扩展到一个连贯的指导学习理论中。该方法利用了计算建模的最新进展，通过直接指导和探索儿童学习的概率模型来提供学习的正式描述。通过这种协作，将形成一种综合理论，解决什么是指导性学习、何时提供指导以及为什么它有效。行为实验将调查儿童在学龄期的指导性学习。实验将把儿童在指导环境中的学习与在直接教学和探索环境中的学习进行对比，以实验测试新的指导学习计算模型的假设和预测。这项研究旨在开发建立在新的、数学上严格的模型基础上的新的教育方法，以实现在真实的教育环境中告知政策选择的目标。该奖项来自学习科学-协作网络(SL-CN)计划，资金来自SBE行为和认知科学部门(BCS)、SBE多学科活动办公室(SMA)和EHR核心研究(ECR)计划。

This Science of Learning Collaborative Network brings together psychology, education, and computer science researchers from Rutgers University, Temple University, Boston University, University of California-Berkeley, and University of Delaware, focused on the question of how children learn best. The network will investigate how to combine direct instruction with exploratory learning in ways that foster learning both immediately and for the long-term. Some previous research shows that direct instruction is best, but other research shows that exploratory learning is better. The debate over which approach is better reflects the absence of a theory of how to combine these methods to yield better learning outcomes than by either approach alone. The research components of this project will combine elements of these approaches to develop a theory of guided learning. The research will inform educational policy by developing novel methods and ways of thinking about education that foster immediate learning while also promoting the engagement and enjoyment that can drive future learning.Debates in education and cognitive development have treated choice between learning methods, such as direct instruction and exploratory play, as an either-or question. The current research aims to integrate the strengths of these approaches and extend them into a coherent theory of guided learning. The approach capitalizes on recent advances in computational modeling that provide formal accounts of learning through direct instruction and exploration in probabilistic models of children's learning. Through this collaborative, an integrated theory that addresses what guided learning is, when to provide guidance, and why it works, will be developed. Behavioral experiments will investigate guided learning in children in the early school years. Experiments will contrast children's learning in guided settings with learning in direct instruction and exploration settings to experimentally test assumptions and predictions of the new computational model of guided learning. This research aims to develop novel educational methods founded on novel, mathematically rigorous models, toward the goal of informing policy choices in authentic educational settings. The award is from the Science of Learning-Collaborative Networks (SL-CN) Program, with funding from the SBE Division of Behavioral and Cognitive Sciences (BCS), the SBE Office of Multidisciplinary Activities (SMA), and the EHR Core Research (ECR) Program.