Educational Robotics: From Computational to Complexity Thinking

Since the seminal work of Seymour Papert, Educational Robotics (ER) has spread across various educational curricula. ER embodies Papert’s constructionism which stresses the importance of learning by making things. According to Papert and Piaget children construct knowledge through an interactive process with the surrounding environment. ER has been widely utilized to foster Computational Thinking (CT), a strategy of problem solving that borrows concepts from computer science. Ironically, Papert warned us about the risk of embracing a specific (programming) language, as it can favor one way of thinking over another. Computer science, however, is a very young discipline, and humans have been coping with complex problems for ages without the possibility to resort to it. In this paper, we describe two bioinspired approaches which have used robotics to teach how to evolve and understand the behavior of groups of robots. The idea we support is that by incorporating Complexity Thinking (CxT) into ER, students not only learn how to decompose tasks (as in CT) but also how to navigate and adapt to complex, decentralized systems, similar to the behavior observed in nature. In this way, the aim is to enrich the learning experience by fostering a deeper understanding of the underlying dynamics.