Ruta de cualificación en Machine Learning para los docentes de matemáticas en una institución educativa pública de Cúcuta
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Fecha
2023
Autores
Celis Pérez, Angie Daniela
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Ediciones Universidad Simón Bolívar
Facultad de Ciencias Jurídicas y Sociales
Facultad de Ciencias Jurídicas y Sociales
Resumen
El objetivo del estudio fue el diseñar una ruta de cualificación a los docentes de una
institución educativa pública de Cúcuta, sobre el uso de las herramientas del Machine
Learning en el proceso enseñanza aprendizaje por medio de un diagrama ¿Cómo? ¿cómo? Se
desarrolló bajo el paradigma interpretativo, así mismo presenta un enfoque cualitativo que se
complementa con un instrumento cuantitativo y tiene un diseño no experimental. Los
informantes fueron docentes de un colegio público de Cúcuta que ejercen la enseñanza de
matemáticas en grados 6 a 9, los instrumentos de recolección fueron una encuesta virtual y
una entrevista semiestructurada presencial. Resultados: los docentes participantes mostraron
un gran interés por dichas herramientas tecnológicas, pero a su vez se generó gran
incertidumbre a raíz de la poca cualificación al respecto y equipamiento en la institución.
Conclusión: Se obtiene una ruta de cualificación donde se considera el impacto que puede
tener el Machine Learning en el aula pensando en los docentes y que puede beneficiar a los
estudiantes, esta ruta propuso diferentes puntos de los cuales destacan las herramientas
digitales como aplicaciones y páginas web que pueden ser utilizadas como apoyo y las
ventajas al momento de la aplicación del machine learning en el aula
The objective of the study was to design a qualification route for teachers in a public educational institution in Cúcuta, on the use of Machine Learning tools in the teaching learning process by means of a How? how? diagram. It was developed under the interpretative paradigm, and also presents a qualitative approach that is complemented by a quantitative instrument and has a non-experimental design. The informants were teachers from a public school in Cúcuta who teach mathematics in grades 6 to 9; the collection instruments were a virtual survey and a semi-structured face-to-face interview. Results: The participating teachers showed great interest in these technological tools, but at the same time great uncertainty was generated due to the lack of training and equipment in the institution. Conclusion: A qualification route is obtained where the impact that Machine Learning can have in the classroom is considered, thinking about the teachers and that it can benefit the students, this route proposed different points of which digital tools such as applications and web pages that can be used as support and the advantages at the time of the application of machine learning in the classroom stand out.
The objective of the study was to design a qualification route for teachers in a public educational institution in Cúcuta, on the use of Machine Learning tools in the teaching learning process by means of a How? how? diagram. It was developed under the interpretative paradigm, and also presents a qualitative approach that is complemented by a quantitative instrument and has a non-experimental design. The informants were teachers from a public school in Cúcuta who teach mathematics in grades 6 to 9; the collection instruments were a virtual survey and a semi-structured face-to-face interview. Results: The participating teachers showed great interest in these technological tools, but at the same time great uncertainty was generated due to the lack of training and equipment in the institution. Conclusion: A qualification route is obtained where the impact that Machine Learning can have in the classroom is considered, thinking about the teachers and that it can benefit the students, this route proposed different points of which digital tools such as applications and web pages that can be used as support and the advantages at the time of the application of machine learning in the classroom stand out.
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Palabras clave
Machine learning, Conocimiento, Herramientas digitales, TIC, Machine learning, Knowledge, Digital tools, TIC