Topics and trends in physics teaching using artificial intelligence
This study investigates the application of machine learning, specifically using the Latent Dirichlet Allocation algorithm, to identify topics and trends in academic journals on Physics education in the Latin America context. The journals analyzed were the Revista Brasileira de Ensino de Física (RBEF...
Guardado en:
| Autores principales: | , , |
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| Formato: | Artículo revista |
| Lenguaje: | Español |
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Asociación de Profesores de Física de la Argentina
2023
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| Materias: | |
| Acceso en línea: | https://revistas.unc.edu.ar/index.php/revistaEF/article/view/43304 |
| Aporte de: |
| Sumario: | This study investigates the application of machine learning, specifically using the Latent Dirichlet Allocation algorithm, to identify topics and trends in academic journals on Physics education in the Latin America context. The journals analyzed were the Revista Brasileira de Ensino de Física (RBEF) and the Revista de Enseñanza de la Física (REF), covering the period from 2001 to 2022. A total of 1664 articles from RBEF and 885 from REF were collected, representing 79% and 85% of the articles published in these periods, respectively. The results indicated dominant topics in each journal and their respective trends over time. For instance, RBEF showed a decline in topics related to Physics Education and an increase in publications on General Physics. In contrast, REF displayed a predominance of topics related to Teaching, with a significant rise in publications about Virtual Laboratory and Teaching. These findings provide valuable insights into the evolution of research themes in Physics education in the Brazilian and Argentine contexts and the potential of using machine learning in Physics education research. |
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