Integrating Machine Learning for the Continuing Education of Science Teachers
Jhon AlÃ©
Journal of Information & Systems Management, Vol. 14, No. 2, 2024.
DOI: https://doi.org/10.6025/jism/2024/14/2/82-89
Link: https://www.dline.info/jism/fulltext/v14n2/jismv14n2_3.pdf
Abstract:
This study investigates the perception of 42 science teachers in Chile after participating in a two-week workshop focused on the curricular integration of Machine Learning technology to enrich their teaching strategies in the Science course. Using KPSI-type Likert surveys, a pre and posttest was administered to assess changes in perception, followed by statistical analysis.

The results highlight significant improvements in teachersâ€™ perception in key areas, such as digital citizenship knowledge, digital resource selection to support their teaching, and more positive attitudes towards the integration of Machine Learning in the classroom. However, significant challenges were identified related to the conceptualization and application of Machine Learning in the educational environment.

This study underscores the need to provide additional support and specific training to overcome barriers to the successful adoption of these technologies in science education. These findings are relevant for the development of effective teacher training strategies and the promotion of successful integration of Machine Learning in educational settings.