Trabajando el pensamiento computacional a través de un Interfaz tangible para la programación de robots

• Main Author: Gonzaga de Mattos Vogel, Bruno
• Publication Date: 2023
• Other Authors: Pinto Pereira, Claudia
• Format: Article
• Language: por
• Source: Revista Brasileira de Informática na Educação
• Download full: https://journals-sol.sbc.org.br/index.php/rbie/article/view/2824
• topic: Tecnologías de la información y la comunicación; Interfaz tangible; Programación de robots; Pensamiento Computacional; Algoritmos de ensenanza y aprendizaje; Tecnologias de Comunicação e Informação; Interface Tangível; Programação de Robôs; Pensamento Computacional; Ensino e aprendizagem de algorimos; Communication and Information Technologies; Tangible Interface; Robot Programming; Computational Thinking; Teaching and learning algorithms

Tangible User Interfaces (TUI) are tools that can fit into a common user interaction environment and propose the representability of virtual objects through physical objects. One of the intentions is to take advantage of the skills that exist in haptic interaction to achieve a certain objective, including teaching and learning. This article proposes the construction of a tangible interface to be used in robot programming, using concepts such as computional thinking (CT) and Bloom’s taxonomy as theoretical bases for teaching and learning. The user interacts with the interface through blocks, each of which has a specific function (front, right, left, back, loop and if). With the blocks properly positioned on a platform, in their execution order, the robot can execute the planned movement. The interface validation was performed with 10 elementary school students, aged eight to nine years. In the result, it was possible to observe a good evaluation for usability, in addition to the application of the concepts of bloom taxonomy. Quantitatively, it was not possible to observe a significant difference in the averages of the tests on the CT (pre and post tests), despite the descriptive statistical measures showing a higher concentration of correct answers in the tests after the experiment. Qualitatively, the experiments showed the use of CT concepts such as abstraction, generalization, logical analysis, evaluation and the very concept of algorithm.