I am in a cafe when a little child throws his mother’s bag in search of fruit snacks. The content spills on the table, bench and floor. It is a chaotic but functional solution to the problem.
Children want an unconventional thinking that may seem messy at first glance. This, apparently chaotic behavior, was the inspiration for the best -known development psychologist Jean Piaget’s theory: that children create their knowledge through experience and have to pass through four consistent stages, the first two of which lack the possibility of using structured logic.
Piaget remains a goat of developmental psychology. He fundamentally and forever changed the world’s attitude towards children, showing that children do not enter the world with the same conceptual elements as adults, but must construct them through experience. No one has made a strange catalog of behavior that researchers can still reproduce in individual children even today.
Although Piaget has really rightly noted that children are engaged in a lot of unusual behavior, my laboratory recently revealed evidence that planned some long -term assumptions about the boundaries of children’s logical capabilities resulting from their work. Our new document in Nature Human Behanse describes how young children are able to find systematic solutions to complex problems without any instructions.
The order of things
In the 1960s, Piaget noted that young children rely on rude testing and errors, rather than systemic strategies, trying to order objects based on a certain constant quantitative dimension, such as length. For example, a 4 -year -old child asked to organize sticks from the shortest to the longest, move them by accident and will usually not achieve the desired final order.
Psychologists have explained the ineffective behavior of young children in such an order assignment-what we call the series task-like indicator that children cannot use systematic strategies related to problems for at least 7 years.
Slightly controversial, my colleagues and I found that by increasing the difficulties and cognitive requirements of the series, young children actually encouraged them to discover and use algorithmic solutions.
A classic Piaget study asked the children to put some visible items such as wooden sticks at height. Huiwen Alex Yang, Doctor of Psychology. Candidate working in computing learning models in my laboratory has caused difficulties in our task version. Advising our coworker Bill Thompson, Yang designed a computer game that demanded that children use feedback to determine the height of the objects hidden on the wall ,.
The game asked the children to order the bunny creatures from the shortest to the highest, clicking on sneakers to change their seats. Beings changed places only if they were in the wrong order; Otherwise, they remained to be placed. Since they could only see the bunny shoes, not their height, the children had to rely on logical conclusions, not to directly observe the task. Yang tried 123 children between 4 and 10 years.
Strategy for clarification
We found that the children independently discovered and applied at least two well -known sorting algorithms. These strategies, called selection sorting and Shaker Sort, are usually studied in computer science.
More than half of the children we tried showed evidence of structured algorithmic thinking and from the age of 4. While older children are more likely to use algorithmic strategies, our findings contradict Piaget’s belief that children are unable to such a systemic strategy up to 7 years. He thought that children first needed to achieve what he called at a specific phase of activity development.
Our results show that children can actually find a spontaneous logical strategy much earlier when circumstances require it. In our task, the test and error strategy could not work as the objects ordered were not directly monitored; The children could not rely on the reviews of perception.
In order to explain our results, you need more nuanced Piaget’s primary data interpretation. Although children can still appreciate less logical solutions to the first two piagetian stages, it is not because they are unable to do differently if the situation requires it.
A systematic approach to life
Algorithmic thinking is very important not only in high -level mathematics classes, but also in everyday life. Imagine you need to bake two dozen biscuits, but your recipe gives only one. You can go through all the recipe action twice by washing the bowl between them, but you will never do it because you know that it would be ineffective. Instead, you would double the ingredients and do each step only once. Algorithmic thinking allows you to determine a systematic way to get closer to the need for twice as much biscuit, which improves your baking efficiency.
Algorithmic thinking is an important ability for children when they learn to move and act in the world – and we now know that they have access to these abilities much earlier than psychologists believed.
The fact that children can engage in algorithmic thinking before formal training have a great influence on STEM – science, technology, engineering and mathematics. Guardians and educators now need to consider when and how they give children the opportunity to solve more abstract problems and concepts. Knowing that children’s minds are ready for structured problems for a preschool, we can foster these abilities earlier, maintaining stronger mathematics and calculation skills.
And have a little patience next time you encounter children interacting with the world in ways that may not be very comfortable. When picking up your belongings from the café floor, remember that all of this is a part of the children constructing their knowledge. Those seemingly chaotic children, soon on the way to obvious logical behavior.
This article has been published from a conversation, non -profit, independent news organizations that provide you with facts and reliable analysis to help you give meaning to our complex world. This was written: Celeste Kidd, California University, Berkeley
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Celeste Kidd receives funding from the National Science Foundation, John Templeton Foundation, Jacobs Foundation and advanced research and invention agencies.