Exploring the Perceptron in Machine Learning for Mathematics Teaching in Professional and Technological Education
DOI:
https://doi.org/10.47236/2594-7036.2026.v10.1924Keywords:
Machine learning, Mathematics teaching, Professional and Technological EducationAbstract
Machine Learning constitutes one of the pillars of Artificial Intelligence, a topic widely discussed today; however, such techniques are generally interpreted as “black-box” systems, which highlights the importance of promoting, within Professional and Technological Education, an understanding of the mathematical foundations that underpin these algorithms and explain how they operate. Thus, the objective of this study is to investigate how Machine Learning fundamentals can be applied in Mathematics teaching, moving beyond a merely technicist approach to technology. To this end, a didactic sequence employing Linear Regression and an Artificial Neuron is proposed, with the aim of supporting integrated high school students in learning topics such as statistics, first-degree equations, and the construction and interpretation of graphs, among others, fostering the development of correlational reasoning. To validate the didactic sequence, data were collected through a questionnaire assessing participants’ prior knowledge, an activity evaluation questionnaire, and a discussion circle. The collected data were examined using thematic analysis. The results indicated that the didactic sequence encouraged the application of the proposed mathematical content and promoted discussion about the use of this technology in society, particularly in the world of work.Downloads
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