How do children learn best? Recent research in learning science suggests that children benefit the most when they engage in guided play, an approach that combines the independence of free play with the structure of direct instruction. In guided play, children are encouraged to take the lead in navigating playful learning environments and controlling their own learning processes. Adults participate in these learning experiences in a supportive role, guiding but not directing.
Because guided play is dynamic and interactive in nature, each interaction between adults and children is highly fluid and unique. “You have to take into account a lot of critical factors to understand what supports learning in guided play. You have to understand the level of knowledge of each learner and the dynamics of the learning environment as they unfold over time, for example,” explains researcher Elizabeth Bonawitz.
One such critical factor is the effectiveness of the guidance a learner receives. Guidance that is effective for one learner may not be effective for another. To illustrate, two children who have different misconceptions about what constitutes a triangle would benefit from different examples that guide them away from their respective misunderstandings. Timing also affects effectiveness. The guidance provided has to be contingent on the learners’ prior actions and mental state at that exact moment – including their level of knowledge, attention, and emotions towards their play partners.
In recently published research, Bonawitz and colleagues proposed a new framework for guided play that takes into account how the content and timing of the guidance every learner receives play a role in determining how well they learn. Creating this framework required integrating computational modelling and data science, which is necessary to address the complexities of guided play.
“What are the optimal environments and practices that facilitate children’s learning in the context of guided play?”
“Because guided play is dynamic, there are a multitude of possible ways in which the various critical factors interact with each other and the learner figures out the world. Data science, and in particular machine learning tools, can analyse these complicated, dynamic, and large sets of possibilities to solve this complex computational problem,” Bonawitz points out.
New models and analytical tools that are specifically designed for this dynamic, interactive learning approach are needed if we are to understand the other critical factors that determine how guided play affects every child differently. “What are the optimal environments and practices that facilitate children’s learning in the context of guided play? With new models, we can identify the practices that will help fulfill the promises of guided play,” explains Bonawitz.
“If used correctly, guided play helps children learn content, and at the same time, they learn how to learn.”
Ensuring that all children achieve the learning outcomes they are capable of when engaging in guided play is a challenging task. But the sooner we are able to develop comprehensive models and a theory of guided play, the sooner we will be able to design more effective educational interventions that can help to close the achievement gap. “If used correctly, guided play helps children learn content, and at the same time, they learn how to learn. It essentially gives children the tools to be successful learners in the future,” concludes Bonawitz.