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

SL-CN：引导式学习：发展、教育和计算视角

基本信息

批准号：
1640816
负责人：
Patrick Shafto
金额：
$ 74.9万
依托单位：
Rutgers University Newark
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640816&HistoricalAwards=false
关键词：
SL CN Guiding guided learning

项目摘要

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.

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州大学的心理学，教育和计算机科学研究人员，专注于儿童如何学习最好的问题。该网络将研究如何将联合收割机直接指导与探索性学习相结合，以促进即时和长期学习。一些先前的研究表明，直接教学是最好的，但其他研究表明，探索性学习更好。关于哪种方法更好的争论反映了如何将这些方法联合收割机结合起来以产生比单独使用任何一种方法更好的学习效果的理论的缺乏。这个项目的研究部分将联合收割机结合这些方法的元素，发展一个指导学习的理论。这项研究将通过开发新的教育方法和思维方式来为教育政策提供信息，这些方法和方式既能促进即时学习，又能促进参与和享受，从而推动未来的学习。教育和认知发展领域的辩论将学习方法（如直接教学和探索性游戏）之间的选择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势，并将其扩展为一个连贯的指导学习理论。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，提供正式的学习帐户。通过这种合作，一个综合的理论，解决什么是指导学习，什么时候提供指导，以及为什么它的工作，将被开发。行为实验将调查儿童在学龄早期的指导学习。实验将对比儿童的学习在引导设置与学习在直接指导和探索设置实验测试的假设和预测的新的计算模型的引导学习。本研究旨在开发基于新颖的数学严谨模型的新颖教育方法，以实现在真实的教育环境中为政策选择提供信息的目标。该奖项来自学习科学合作网络（SL-CN）计划，由SBE行为和认知科学部（BCS），SBE多学科活动办公室（SMA）和EHR核心研究（ECR）计划资助。

项目成果

期刊论文数量（53）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accessing the Inaccessible: Redefining Play as a Spectrum.

DOI：
10.3389/fpsyg.2018.01124
发表时间：
2018
期刊：
Frontiers in psychology
影响因子：
3.8
作者：
Zosh JM;Hirsh-Pasek K;Hopkins EJ;Jensen H;Liu C;Neale D;Solis SL;Whitebread D
通讯作者：
Whitebread D

Whole Number Bias in Children's Probability Judgments

儿童概率判断中的整数偏差

DOI：
发表时间：
2018
期刊：
Annual Meeting of the Cognitive Science Society
影响因子：
0
作者：
Shaun O'Grady;Fei Xu
通讯作者：
Fei Xu

Optimal Cooperative Inference

DOI：
发表时间：
2017-05
期刊：
影响因子：
0
作者：
Scott Cheng-Hsin Yang;Yue Yu;A. Givchi;Pei Wang;Wai Keen Vong;Patrick Shafto
通讯作者：
Scott Cheng-Hsin Yang;Yue Yu;A. Givchi;Pei Wang;Wai Keen Vong;Patrick Shafto

Preschoolers are more likely to direct questions to adults than to other children (or selves) during spontaneous conversational acts

在自发的对话行为中，学龄前儿童更有可能向成人提出问题，而不是向其他孩子（或自己）提出问题