The contribution of cognitive and non-cognitive predictors on academic achievement among students at a public higher learning institution
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Date
2009
Authors
Syed Ahmad, Sharifah Amnah
Journal Title
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Abstract
The purpose of this study is to identify the contribution of cognitive and
non-cognitive predictors on academic achievement among students at a public
higher learning institution. The predictors consist of demography, intelligence,
thinking styles and learning approaches. 625 students were chosen as the samples
of the study. This quantitative study employs a survey technique. Three
instruments were translated into Malay Language and used to collect data;
Sternberg Triarchic Abilities Test (STAT) (Sternberg, 1993), Thinking Style
Inventory (TSI) (Sternberg & Wagner, 1992), Revised Study Process
Questionnaire (R-SPQ-2F) (Biggs, Kember & Leung, 2001) and demographic
form. One way Anova, Pearson correlation and stepwise multiple regression
analysis were the three statistical analysis used to answer the research hypothesis.
The significant level of p<.05 was fixed for research hypothesis testing purpose.
Results showed that there were no significant differences between demographic
characteristics; birth order F(2, 625) =0.55, p=.947), parents’ education level (F(2, 622)
=.156, p=.855) and socio-economic status (F(2, 622) =.348, p=.707) with academic
achievement. Results for Pearson correlation yielded values of r between -.002 to
.93. Stepwise multiple regression analysis results for intelligence showed that the
variance accounted for by practical intelligence on academic achievement was
28.1% (R2=.281). Findings from stepwise multiple regression analysis on each of
the individual domains of thinking styles revealed that certain thinking styles
contributed significantly to academic achievement. The variance accounted for by
the thinking styles were 27.5% (R2=.275) for the domain of scope (internal
thinking style), 17.5% (R2=.175) for the domain of function (executive thinking
style), 21.7% (R2=.217) for the domain of form (monarchic thinking style), 24.4%
(R2=.244) for the domain of level (global thinking style) and 28.0% (R2=.280) for
the domain of tendency (liberal thinking style). Results from stepwise multiple
regression analysis for the overall combinations of the five domains (Scope,
Form, Function, Level, Tendency) indicated that the highest variance was
contributed by liberal thinking style (the domain of level) which accounted for by
28.0% (R2=.280) compared to other combinations of the domains which
contributed minimally to academic achievement. Results from stepwise multiple
regression analysis on learning approaches (deep and surface) indicated that the
deep learning approach was the highest contributor for academic achievement
accounting for a variance of 30.2% (R2=.302) in the data compared to the surface
learning approach. Lastly, stepwise multiple regression analysis results for all
predictors (intelligence, thinking styles, learning approaches and demography)
showed that the deep learning approach was the highest contributor for academic
achievement with a variance of 30.2% (R2 =.302) among samples compared to the
other combinations of predictors. In general, it was concluded that academic
achievement is closely related to the deep learning approach followed by practical
intelligence and executive thinking style. However, the other combinations of
predictors only contributed minimally to academic achievement.
Description
PhD
Keywords
Education , Academic achievement , Higher learning institution