Die voorspelling van akademiese sukses by eerstejaar-technikonstudente

English: The shortage of high-level person power is a world-wide problem. Serious economic problems are also experienced in South Africa as a result of this. Higher education plays a central role in meeting South Africa's social and economic needs. The Department of Education sets broad higher education objectives that regard the transformation of higher education as part of South Africa's pursuit of social justice and progress of the economy as a whole. The relation between higher education and South Africa's economic and socialprogress was explained in this study within the framework of the general systemstheory. In the pursuit of achieving higher educational institution objectives, higher educational institutions frequently use selection processes. The challenge that higher educational institutions are currently facing is to develop selection models that will succeed in identifying talented individuals and in effectively predicting future academic success. The problem regarding selection is complex in a multicultural country like South Africa, because unequal opportunities and different backgrounds are substantial realities. New selection models must be developed to accommodatethe changing cultural composition of South Africa. As Technikon Free State is confronted with overwhelming numbers of students and unequal backgrounds, a new selection model was used which takes into account general scholastic aptitude as measured by the General Scholastic Aptitude Test (GSAT),language skills as measured by the English Proficiency Test (EPT),and the number of subjects for which students registered. The problem statement of this study is the determination of a selection model that could predict possible academic success for first-year technikon students. This study determines to what extent the predictor variables, namely matric results, GSAT-counts,EPT-countsand the number of subjects for which students register can be utilised to predict the criterion variable, namely academic success, in the Faculties of Management, Engineering, Human Science and Applied Sciences. A step-wise regression analysis was done to determine the above. It appeared from the results that matric performance was the variable that correlated most highly with academic success. This trend was indicated mainly for the total group. The regression equation indicated that this variable correctly predicted approximately 13 % of the total variance of the criterion variable.