REPRESENTATIONAL FLUENCY AND HOW IT CAN BE DEVELOPED IN PRIMARY SCHOOL STUDENTS

Abstract

The ability to reason and work among multiple representations, also known as representational fluency, is an important foundational ability that students need to succeed in courses like math and science. This thesis covers a multi-year project that aims to develop an online-learning system to teach and train students on the competences of representational fluency and to offer effective remedial interventions for primary level students who have representational fluency weaknesses. During our research project, we identified nine competences that form the construct of representational fluency for primary school students and developed a new Representational Fluency Test (RFT). We also developed a novel adaptive algorithm based on the Rasch analysis to generate adaptive training quizzes for students. We also conducted two experiments to investigate the effectiveness of different multimedia types that can be used in teaching representational fluency competences, and to discover the best approach to give students training practices.