SL-CN: GUIDING GUIDED LEARNING: DEVELOPMENTAL, EDUCATIONAL AND COMPUTATIONAL PERSPECTIVES

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.