The contribution of cognitive and non-cognitive predictors on academic achievement among students at a public higher learning institution
| dc.contributor.author | Syed Ahmad, Sharifah Amnah | |
| dc.date.accessioned | 2014-11-03T02:12:33Z | |
| dc.date.available | 2014-11-03T02:12:33Z | |
| dc.date.issued | 2009 | |
| dc.description | PhD | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/234 | |
| dc.language.iso | en | en_US |
| dc.subject | Education | en_US |
| dc.subject | Academic achievement | en_US |
| dc.subject | Higher learning institution | en_US |
| dc.title | The contribution of cognitive and non-cognitive predictors on academic achievement among students at a public higher learning institution | en_US |
| dc.type | Thesis | en_US |
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