Pixel International Conferences

Science students are required to apply mathematical knowledge at various levels. However, many students could not really relate what they have learned in mathematics classes in solving problems to another context. As such, this paper explore the factors that influence the ability of university science students to transfer their mathematical knowledge to a range of scientific contexts. The instrument was designed and comprised of mathematics questions and transfer questions. There are 255 university science students involved in this study. The performances of the students have been identified by determining the overall transfer score. The Binary Logistic Regression model was used to identify the possible factors associated with students that would influence the knowledge transfer. The factors are namely Cumulative Grade Point Average (CGPA), gender, age, educational qualification, faculty and mathematics score. In this study, CGPA, faculty and mathematics score were found to be significantly affected the overall transfer score.

Keywords: Knowledge transfer, Binary Logistic Regression, Transfer score, Transfer questions;