Predicting Academic Achievement in Prospective Teachers

The aim of this study was to investigate the role of prospective teachers' learning strategies and their individual characteristics in predicting academic achievement. The participants were 265 studentsstudying in different major fields at the Faculty of Education in Pamukkale University. Learning Strategies Inventory and Personal Information Sheet were used to gather data. Results showed that academic achievement was predicted by deep learning strategies, grade, gender and age. It was also seen that prospective teachers used deep learning strategies more than surface learning strategies, deep learning strategies were positively correlated with academic achievement, surface learning strategies did significiantly differ as to gender, while metacognitive learning strategies did significiantly differ as to major field. Implications of the findings were discussed and suggestions were given for the educators