The Investigation of Teacher Candidates' Learning Approaches and Engagement in a Hybrid Learning Environment According to RASE Model

Abstract

This study aimed to investigate whether teacher candidates' learning approaches and engagement levels predicted their achievement in the Curriculum Development course in a hybrid course in Turkey. This study was designed according to RASE (Resources/Activity/Support/Evaluation) model. In this study, data were collected from 129 teacher candidates through the 'Learning Approaches Questionnaire' and 'Engagement Questionnaire'. The achievement scores of teacher candidates were obtained at the end of the semester according to their course grades. To answer the research question, the Multiple Linear Regression analysis was employed. The results of the study showed that while the deep learning approach of teacher candidates was significantly and positively related to engagement variables, the surface learning approach was related to engagement variables negatively. However, it was revealed that the surface learning approaches and behavioral engagement of teacher candidates significantly predicted the achievement in the hybrid Curriculum Development course. It can be concluded that the learning environment is important for learning outcomes. It can be suggested that besides providing different active learning opportunities, teacher candidates should be assessed in accordance with the level of applications conducted in the hybrid learning course to improve deep learning and all types of engagement levels.