Çevrimiçi Matematik Öğrenmede Öz-Düzenlemeyi Etkileyen Bileşenler: Deprem Sürecinde Bir Öz-Düzenleme Perspektifi.

ÇELİK, Halil Coşkun; KASAP, Yusuf

The aim of this research is to determine the variables that affect self-regulated learning in the online mathematics learning process and to examine the relationship between these variables. The study, designed in the relational screening model, was conducted on a total of 233 prospective mathematics teachers taking online courses at a state university in Turkey. Teacher candidates filled out data collection tools online, including a personal information form, selfregulated online learning scale and online learning self-efficacy scale. Data were analyzed using random forest and classification and regression tree methods. The correct classification performance of the model created by the random forest method was found to be very high. Among the variables that are effective in predicting teacher candidates' self-regulation online learning, the ones with the highest importance are self-efficacy, self-evaluation and grade level variables. Other important variables were determined as course preference, level of internet use and frequency of internet use. In line with the results obtained, it is thought that this study will make a significant contribution to making predictions about the development of online self-regulation learning of teacher candidates, guiding their self-regulated and development of self-efficacy in the online learning process.

SELF-regulated learning; STUDENT teachers; LEARNING; MATHEMATICS teachers; RANDOM forest algorithms

Gazi University Journal of Gazi Educational Faculty (GUJGEF), 2024, Vol 44, Issue 3, p1785

1301-9058

Academic Journal

10.17152/gefad.1455657