Research in Engineering Education Symposium 2011 - rees2009

Universidad Politécnica de Madrid (UPM) Página 828 de 957
for measuring the intention factors as it is frequently used to assess learning behaviour
associated with learner autonomy.
Results
Analysis of Correlation Coefficient
Results of the Pearson correlation coefficient analyses are shown in Table 1. The
interpretation of the strength of the relationship among variables is made with reference
to Cohen (1988) and Cohen, Cohen, West, & Aiken, (2003).
Table 1: Person Correlation analysis (MY, N=136; AU, N=122)
It is apparent from Table 1 that strategy, interest and intention have a statistically
significant and positive correlation with academic performance (UCGPA) of Australian and
Malaysian students. There were also positively strong and statistically significant
correlations between strategy and interest (rMY = 0.774; rAU=0.692), strategy and intention
(rMY = 0.542; rAU = 0.420), and interest and intention (rMY = 0.593; rAU=0.455). These
findings were consistent with Riggs & Gholar (2009)’s point of view that the cognitive,
affective and conative domains are closely interconnected and interdependent.
Analysis of Multiple Regressions
The theory of reasoned action (Fishbein & Ajzen, 1975) was used as a framework to test
whether learning strategy, learning interest and learning intention can predict academic
performance of engineering undergraduates in Australia and Malaysia. The three
predictor variables were inserted into the regression model using enter method of SPSS.
The results obtained from the multiple regression analysis are presented in
Table 2. Table 2: Results of Multiple Regression Analyses (Enter Method)
Results in Table 2 shows that despite strong relationships between strategy, interest,
intention and academic performance, results of the multiple regression analysis reveals
Proceedings of Research in Engineering Education Symposium 2011
Madrid, 4 th - 7 th October 2011