JUA - CECOMP - Engenharia da Computação

URI Permanente desta comunidadehttps://repositorio.univasf.edu.br/handle/123456789/152

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Navegando JUA - CECOMP - Engenharia da Computação por Autor "de Melo Queiroz, Ana Emilia"

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    Design de Interfaces Inteligentes para o Ensino de Matemática
    (2026-06-26) de Melo Queiroz, Ana Emilia
    This report presents a proposal for the analysis and organization of intelligent interfaces for Mathematics education, focusing on the learning of additive structures through problem situations, symbolic representations, visual feedback, and Multiagent Systems. The study is based on the Theory of Conceptual Fields, Instrumental Analysis, and research on educational interfaces, considering that learning occurs in the relationship among the user, the task, the interface artifacts, and the concepts mobilized during problem solving. The investigation analyzes the Gerard interface, designed for teacher training in the teaching of additive structures. The methodology involved the use of a paper prototype, tested with users while solving problems involving composition, transformation, and comparison of measures. The users’ actions were recorded, coded, and analyzed in their material and cognitive dimensions, observing the errors committed, the feedback provided, and the theorems-in-action mobilized during the task. The results indicate that the most frequent errors were related to the positioning of elements in the diagram and to the use of the sign of the relative number. It was also observed that certain types of feedback can favor the reorganization of users’ actions, whereas inadequate messages may reinforce mistaken interpretations. The analysis made it possible to identify relationships among types of tasks, manipulated artifacts, users’ level of schooling, and the emergence of operational invariants. It is concluded that the detailed analysis of interactive situations can contribute to the planning of a Multiagent System capable of offering contextualized help, monitoring the user’s progress, and gradually removing feedback as the user develops autonomy. Thus, the report provides preliminary support for the construction of interaction models that are better adjusted to users’ cognitive needs in educational interfaces.