
This study on mathematical modelling (MM) in science education aims to examine the mathematical modelling competencies (MMC) of pre-service science teachers and teachers. For this purpose, a qualitative study was conducted with 12 pre-service science teachers and teachers. A case study design was used in the research. The study's data were obtained through activity forms, interviews and observation techniques. Descriptive analysis was used to determine the MMY by considering the sub-competencies revised by Çakmak (2019) in line with Borromeo Ferri's (2006) MMC. When MMY was examined, it was concluded that MMC developed in the process, the most successful MMY was comprehension, and the most unsuccessful MMC was simplification and construction. Contrary to the literature, it was determined that reaching real conclusions from mathematical results was fine. It is seen that newly graduated and experienced science teachers have an equal level of MMC, while pre-service science teachers have less MMC. In the process, competence was exhibited least in biology and chemistry and most in physics. It is recommended to increase MM studies for science education and to provide MM courses at a minimum undergraduate level and in-service training to experience MM practices.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
