Research in Engineering Education Symposium 2011 - rees2009

Universidad Politécnica de Madrid (UPM) Página 829 de 957
unexpected outcomes. The three learning factors only predict about 3.2% of the academic
performance variance (R 2 = 0.032) for Australian and 8.9% (R 2 = 0.089) of academic
performance variance for Malaysian students. For Malaysian students, the only significant
variable is strategy (βMY = 0.398) while for Australian students, there is no significant
predictor variable for academic performance. Such results do not necessarily discount the
importance of learning strategy, learning motive and learning intention to predict
student’s academic performance in engineering. The next hypothesis is that the
differences between male and female students’ approach towards learning may
contributes to such results. A backward multiple regression analysis was performed to
remove any weak predictor variables in the model. The results are shown in Table 3.
Table 3: Results of Multiple Regression Analyses (Backwards Method)
Table 3 show results of multiple regression analysis (backward elimination) based on
gender function in the two locations, Australia and Malaysia. The selected regression
model (as highlighted) suggesting that the learning factors found are comparable between
gender in the two locations.
In Australia, learning was interest the most significant variable to predict male students’
academic performance (R 2 = 0.074, F(1,91) = 7.25, p