The relationship between the chronotypes and learning approaches and academic achievement of science teachers

In this master thesis, it was carried out to determine the relationships between science teacher candidates chronotypes, learning approaches and academic achievement. In this study, the relational screening model was used. The study was carried out with 223 teacher candidates who were studying at Department of Mathematics Science Education in Akdeniz University during the 2018-2019 academic calendar. For data collection, Organized Two-Factor Study Process Scale and A self-Assessment Questionnaire to Determine Morningness-Eveningness in Human Circadian Rhythms Scale were used to determine the learning approaches and chronotypes of the teacher candidates respectively. Independent Groups T-test, One-Way Variance Analysis (ANOVA) and Regression Analysis were applied to analyze the data. The effects of chronotype and learning approach on the academic achievement (GPA), verbal courses (Turkish language, History of Revolution) and scientific courses (mathematics, physics) were investigated separately. Result shown that GPA and scientific courses success of morning types were found to be more successful than evening types. It was seen that chronotype score and gender variables were statistically significant predictors of the academic achievement. It was observed that as the tendency towards morning types increased, academic achievement increased too, in addition it was found that the academic success of women was higher than men. A significant relationship was found between learning approaches and success in scientific courses. It was found that the scientific success of the deep learners was significantly higher than of the surface learners. When the relationship between chronotype and learning approach was examined, it was seen that morning and intermediate type ones preferred deep learning approach more than that of the evening type ones. It was also seen that chronotype score and gender are statistically significant predictive variables of the deep learning approach. It was found that as the tendency to the morning type increased, the probability of preferring deep learning increased too, and it was also found that men were more likely to prefer the deep learning approach than women. It was observed that both chronotype score and learning approach are significant predictors of physics course success. The increase in the chronotype score (approaching the morning type) resulted in adopting the deep learning approach and increased the success in physics course.